diff --git a/_images/ACER_alg1.png b/_images/ACER_alg1.png new file mode 100644 index 0000000000000000000000000000000000000000..c0b0466759aa2d58af6d7862da20a93c3ca70a07 Binary files /dev/null and b/_images/ACER_alg1.png differ diff --git a/_images/ACER_alg2.png b/_images/ACER_alg2.png new file mode 100644 index 0000000000000000000000000000000000000000..a58d12fb1e60f0d4fe1e6bdecea0f910f56fa3dd Binary files /dev/null and b/_images/ACER_alg2.png differ diff --git a/_images/ACER_alg3.png b/_images/ACER_alg3.png new file mode 100644 index 0000000000000000000000000000000000000000..84ee66165b02182cda2236cabb0951a0852edef2 Binary files /dev/null and b/_images/ACER_alg3.png differ diff --git a/_modules/ding/league/base_league.html b/_modules/ding/league/base_league.html index 2d64630f6540a145fb23db4547dca4d3f17e9f01..7d4315a77328bc022e993c83a060a1c93fef9fea 100644 --- a/_modules/ding/league/base_league.html +++ b/_modules/ding/league/base_league.html @@ -168,7 +168,9 @@ from ding.league.player import ActivePlayer, HistoricalPlayer, create_player from ding.league.shared_payoff import create_payoff -from ding.utils import import_module, read_file, save_file, LockContext, LockContextType, LEAGUE_REGISTRY +from ding.utils import import_module, read_file, save_file, LockContext, LockContextType, LEAGUE_REGISTRY, \ + deep_merge_dicts +from .metric import LeagueMetricEnv
[docs]class BaseLeague: @@ -188,6 +190,41 @@ cfg.cfg_type = cls.__name__ + 'Dict' return cfg + config = dict( + league_type='base', + import_names=["ding.league.base_league"], + # ---player---- + # "player_category" is just a name. Depends on the env. + # For example, in StarCraft, this can be ['zerg', 'terran', 'protoss']. + player_category=['default'], + # Support different types of active players for solo and battle league. + # For solo league, supports ['solo_active_player']. + # For battle league, supports ['battle_active_player', 'main_player', 'main_exploiter', 'league_exploiter']. + # active_players=dict(), + # "use_pretrain" means whether to use pretrain model to initialize active player. + use_pretrain=False, + # "use_pretrain_init_historical" means whether to use pretrain model to initialize historical player. + # "pretrain_checkpoint_path" is the pretrain checkpoint path used in "use_pretrain" and + # "use_pretrain_init_historical". If both are False, "pretrain_checkpoint_path" can be omitted as well. + # Otherwise, "pretrain_checkpoint_path" should list paths of all player categories. + use_pretrain_init_historical=False, + pretrain_checkpoint_path=dict(default='default_cate_pretrain.pth', ), + # ---payoff--- + payoff=dict( + # Supports ['battle'] + type='battle', + decay=0.99, + min_win_rate_games=8, + ), + metric=dict( + mu=0, + sigma=25 / 3, + beta=25 / 3 / 2, + tau=0.0, + draw_probability=0.02, + ), + ) + def __init__(self, cfg: EasyDict) -> None: """ Overview: @@ -195,16 +232,19 @@ Arguments: - cfg (:obj:`EasyDict`): League config. """ - self.cfg = cfg + self.cfg = deep_merge_dicts(self.default_config(), cfg) self.path_policy = cfg.path_policy if not osp.exists(self.path_policy): os.mkdir(self.path_policy) self.league_uid = str(uuid.uuid1()) + # TODO dict players self.active_players = [] self.historical_players = [] self.player_path = "./league" self.payoff = create_payoff(self.cfg.payoff) + metric_cfg = self.cfg.metric + self.metric_env = LeagueMetricEnv(metric_cfg.mu, metric_cfg.sigma, metric_cfg.tau, metric_cfg.draw_probability) self._active_players_lock = LockContext(type_=LockContextType.THREAD_LOCK) self._init_players() @@ -219,7 +259,9 @@ for i in range(n): # This type's active player number name = '{}_{}_{}'.format(k, cate, i) ckpt_path = osp.join(self.path_policy, '{}_ckpt.pth'.format(name)) - player = create_player(self.cfg, k, self.cfg[k], cate, self.payoff, ckpt_path, name, 0) + player = create_player( + self.cfg, k, self.cfg[k], cate, self.payoff, ckpt_path, name, 0, self.metric_env.create_rating() + ) if self.cfg.use_pretrain: self.save_checkpoint(self.cfg.pretrain_checkpoint_path[cate], ckpt_path) self.active_players.append(player) @@ -240,6 +282,7 @@ self.cfg.pretrain_checkpoint_path[cate], name, 0, + self.metric_env.create_rating(), parent_id=parent_name ) self.historical_players.append(hp) @@ -301,7 +344,7 @@ player = self.active_players[idx] if force or player.is_trained_enough(): # Snapshot - hp = player.snapshot() + hp = player.snapshot(self.metric_env) self.save_checkpoint(player.checkpoint_path, hp.checkpoint_path) self.historical_players.append(hp) self.payoff.add_player(hp) @@ -357,7 +400,22 @@ - job_info (:obj:`dict`): A dict containing job result information. """ # TODO(nyz) more fine-grained job info - self.payoff.update(job_info)
+ self.payoff.update(job_info) + if 'eval_flag' in job_info and job_info['eval_flag']: + home_id, away_id = job_info['player_id'] + home_player, away_player = self.get_player_by_id(home_id), self.get_player_by_id(away_id) + job_info_result = job_info['result'] + if isinstance(job_info_result[0], list): + job_info_result = sum(job_info_result, []) + home_player.rating, away_player.rating = self.metric_env.rate_1vs1( + home_player.rating, away_player.rating, result=job_info_result + ) + + def get_player_by_id(self, player_id: str) -> 'Player': # noqa + if 'historical' in player_id: + return [p for p in self.historical_players if p.player_id == player_id][0] + else: + return [p for p in self.active_players if p.player_id == player_id][0]
[docs] @staticmethod def save_checkpoint(src_checkpoint, dst_checkpoint) -> None: diff --git a/_modules/ding/league/one_vs_one_league.html b/_modules/ding/league/one_vs_one_league.html index 8ad578afb1bb85b58ed74262193b95ffeb565edd..26734d75081325ecb24aab8631affda5c9e651f2 100644 --- a/_modules/ding/league/one_vs_one_league.html +++ b/_modules/ding/league/one_vs_one_league.html @@ -214,6 +214,13 @@ decay=0.99, min_win_rate_games=8, ), + metric=dict( + mu=0, + sigma=25 / 3, + beta=25 / 3 / 2, + tau=0.0, + draw_probability=0.02, + ), ) # override diff --git a/_modules/ding/league/player.html b/_modules/ding/league/player.html index 8a89dac3fc3561f6a3c2bb0db61faba8ef1d6ac7..9540da625e2d67ed73942ee30cab2adb910b9e47 100644 --- a/_modules/ding/league/player.html +++ b/_modules/ding/league/player.html @@ -186,7 +186,8 @@ init_payoff: 'BattleSharedPayoff', # noqa checkpoint_path: str, player_id: str, - total_agent_step: int + total_agent_step: int, + rating: 'PlayerRating', # noqa ) -> None: """ Overview: @@ -200,6 +201,7 @@ - player_id (:obj:`str`): Player id in string format. - total_agent_step (:obj:`int`): For active player, it should be 0; \ For historical player, it should be parent player's ``_total_agent_step`` when ``snapshot``. + - rating (:obj:`PlayerRating`): player rating information in total league """ self._cfg = cfg self._category = category @@ -208,7 +210,8 @@ assert isinstance(player_id, str) self._player_id = player_id assert isinstance(total_agent_step, int), (total_agent_step, type(total_agent_step)) - self._total_agent_step = total_agent_step
+ self._total_agent_step = total_agent_step + self._rating = rating @property def category(self) -> str: @@ -232,7 +235,15 @@ @total_agent_step.setter def total_agent_step(self, step: int) -> None: - self._total_agent_step = step + self._total_agent_step = step + + @property + def rating(self) -> 'PlayerRating': # noqa + return self._rating + + @rating.setter + def rating(self, _rating: 'PlayerRating') -> None: # noqa + self._rating = _rating
[docs]@PLAYER_REGISTRY.register('historical_player') @@ -329,10 +340,12 @@ else: return False - def snapshot(self) -> HistoricalPlayer: + def snapshot(self, metric_env: 'LeagueMetricEnv') -> HistoricalPlayer: # noqa """ Overview: Generate a snapshot historical player from the current player, called in league's ``_snapshot``. + Argument: + - metric_env (:obj:`LeagueMetricEnv`): player rating environment, one league one env Returns: - snapshot_player (:obj:`HistoricalPlayer`): new instantiated historical player @@ -348,6 +361,7 @@ path, self.player_id + '_{}_historical'.format(int(self._total_agent_step)), self._total_agent_step, + metric_env.create_rating(mu=self.rating.mu), parent_id=self.player_id ) diff --git a/_modules/ding/policy/acer.html b/_modules/ding/policy/acer.html new file mode 100644 index 0000000000000000000000000000000000000000..bce2c642b8be7bb53ecd0216ab3729d05fe78651 --- /dev/null +++ b/_modules/ding/policy/acer.html @@ -0,0 +1,680 @@ + + + + + + + + + + + ding.policy.acer — DI-engine 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for ding.policy.acer

+from collections import namedtuple
+from typing import List, Dict, Any, Tuple
+import copy
+
+import torch
+
+from ding.model import model_wrap
+from ding.rl_utils import get_train_sample, compute_q_retraces, acer_policy_error,\
+     acer_value_error, acer_trust_region_update
+from ding.torch_utils import Adam, RMSprop, to_device
+from ding.utils import POLICY_REGISTRY
+from ding.utils.data import default_collate, default_decollate
+from ding.policy.base_policy import Policy
+
+EPS = 1e-8
+
+
+
[docs]@POLICY_REGISTRY.register('acer')
+class ACERPolicy(Policy):
+    r"""
+    Overview:
+        Policy class of ACER algorithm.
+
+    Config:
+        == ======================= ======== ============== ===================================== =======================
+        ID Symbol                  Type     Default Value  Description                           Other(Shape)
+        == ======================= ======== ============== ===================================== =======================
+        1  ``type``                str      acer           | RL policy register name, refer to   | this arg is optional,
+                                                           | registry ``POLICY_REGISTRY``        | a placeholder
+        2  ``cuda``                bool     False          | Whether to use cuda for network     | this arg can be diff-
+                                                           |                                     | erent from modes
+        3  ``on_policy``           bool     False          | Whether the RL algorithm is
+                                                           | on-policy or off-policy
+        4  ``trust_region``        bool     True           | Whether the RL algorithm use trust  |
+                                                           | region constraint                   |
+        5  ``trust_region_value``  float    1.0            | maximum range of the trust region   |
+        6  ``unroll_len``          int      32             | trajectory length to calculate
+                                                           | Q retrace target
+        7   ``learn.update``       int      4              | How many updates(iterations) to     | this args can be vary
+            ``per_collect``                                | train after collector's one         | from envs. Bigger val
+                                                           |  collection. Only                   |
+                                                           | valid in serial training            | means more off-policy
+        8   ``c_clip_ratio``       float    1.0            | clip ratio of importance weights    |
+        == ======================= ======== ============== ===================================== =======================
+    """
+    unroll_len = 32
+    config = dict(
+        type='acer',
+        cuda=False,
+        # (bool) whether use on-policy training pipeline(behaviour policy and training policy are the same)
+        # here we follow ppo serial pipeline, the original is False
+        on_policy=False,
+        priority=False,
+        # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
+        priority_IS_weight=False,
+        learn=dict(
+            # (str) the type of gradient clip method
+            grad_clip_type=None,
+            # (float) max value when ACER use gradient clip
+            clip_value=None,
+            # (bool) Whether to use multi gpu
+            multi_gpu=False,
+            # (int) collect n_sample data, train model update_per_collect times
+            # here we follow ppo serial pipeline
+            update_per_collect=4,
+            # (int) the number of data for a train iteration
+            batch_size=16,
+            # (float) loss weight of the value network, the weight of policy network is set to 1
+            value_weight=0.5,
+            # (float) loss weight of the entropy regularization, the weight of policy network is set to 1
+            entropy_weight=0.0001,
+            # (float) discount factor for future reward, defaults int [0, 1]
+            discount_factor=0.9,
+            # (float) additional discounting parameter
+            lambda_=0.95,
+            # (int) the trajectory length to calculate v-trace target
+            unroll_len=unroll_len,
+            # (float) clip ratio of importance weights
+            c_clip_ratio=10,
+            trust_region=True,
+            trust_region_value=1.0,
+            learning_rate_actor=0.0005,
+            learning_rate_critic=0.0005,
+            target_theta=0.01
+        ),
+        collect=dict(
+            # (int) collect n_sample data, train model n_iteration times
+            n_sample=16,
+            # (int) the trajectory length to calculate v-trace target
+            unroll_len=unroll_len,
+            # (float) discount factor for future reward, defaults int [0, 1]
+            discount_factor=0.9,
+            gae_lambda=0.95,
+            collector=dict(
+                type='sample',
+                collect_print_freq=1000,
+            ),
+        ),
+        eval=dict(evaluator=dict(eval_freq=200, ), ),
+        other=dict(replay_buffer=dict(
+            replay_buffer_size=1000,
+            max_use=16,
+        ), ),
+    )
+
+    def _init_learn(self) -> None:
+        r"""
+        Overview:
+            Learn mode init method. Called by ``self.__init__``.
+            Initialize the optimizer, algorithm config and main model.
+        """
+        # Optimizer
+        self._optimizer_actor = Adam(
+            self._model.actor.parameters(),
+            lr=self._cfg.learn.learning_rate_actor,
+            grad_clip_type=self._cfg.learn.grad_clip_type,
+            clip_value=self._cfg.learn.clip_value
+        )
+        self._optimizer_critic = Adam(
+            self._model.critic.parameters(),
+            lr=self._cfg.learn.learning_rate_critic,
+        )
+
+        self._target_model = copy.deepcopy(self._model)
+        self._target_model = model_wrap(
+            self._target_model,
+            wrapper_name='target',
+            update_type='momentum',
+            update_kwargs={'theta': self._cfg.learn.target_theta}
+        )
+        self._learn_model = model_wrap(self._model, wrapper_name='base')
+
+        self._action_shape = self._cfg.model.action_shape
+        self._unroll_len = self._cfg.learn.unroll_len
+
+        # Algorithm config
+        self._priority = self._cfg.priority
+        self._priority_IS_weight = self._cfg.priority_IS_weight
+        self._value_weight = self._cfg.learn.value_weight
+        self._entropy_weight = self._cfg.learn.entropy_weight
+        self._gamma = self._cfg.learn.discount_factor
+        # self._rho_clip_ratio = self._cfg.learn.rho_clip_ratio
+        self._c_clip_ratio = self._cfg.learn.c_clip_ratio
+        # self._rho_pg_clip_ratio = self._cfg.learn.rho_pg_clip_ratio
+        self._use_trust_region = self._cfg.learn.trust_region
+        self._trust_region_value = self._cfg.learn.trust_region_value
+        # Main model
+        self._learn_model.reset()
+        self._target_model.reset()
+
+    def _data_preprocess_learn(self, data: List[Dict[str, Any]]):
+        """
+        Overview:
+            Data preprocess function of learn mode.
+            Convert list trajectory data to to trajectory data, which is a dict of tensors.
+        Arguments:
+            - data (:obj:`List[Dict[str, Any]]`): List type data, a list of data for training. Each list element is a \
+            dict, whose values are torch.Tensor or np.ndarray or dict/list combinations, keys include at least 'obs',\
+             'next_obs', 'logit', 'action', 'reward', 'done'
+        Returns:
+            - data (:obj:`dict`): Dict type data. Values are torch.Tensor or np.ndarray or dict/list combinations. \
+        ReturnsKeys:
+            - necessary: 'logit', 'action', 'reward', 'done', 'weight', 'obs_plus_1'.
+            - optional and not used in later computation: 'obs', 'next_obs'.'IS', 'collect_iter', 'replay_unique_id', \
+                'replay_buffer_idx', 'priority', 'staleness', 'use'.
+        ReturnsShapes:
+            - obs_plus_1 (:obj:`torch.FloatTensor`): :math:`(T * B, obs_shape)`, where T is timestep, B is batch size \
+                and obs_shape is the shape of single env observation
+            - logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim
+            - action (:obj:`torch.LongTensor`): :math:`(T, B)`
+            - reward (:obj:`torch.FloatTensor`): :math:`(T+1, B)`
+            - done (:obj:`torch.FloatTensor`): :math:`(T, B)`
+            - weight (:obj:`torch.FloatTensor`): :math:`(T, B)`
+        """
+        data = default_collate(data)
+        if self._cuda:
+            data = to_device(data, self._device)
+        data['weight'] = data.get('weight', None)
+        # shape (T+1)*B,env_obs_shape
+        data['obs_plus_1'] = torch.cat((data['obs'] + data['next_obs'][-1:]), dim=0)
+        data['logit'] = torch.cat(
+            data['logit'], dim=0
+        ).reshape(self._unroll_len, -1, self._action_shape)  # shape T,B,env_action_shape
+        data['action'] = torch.cat(data['action'], dim=0).reshape(self._unroll_len, -1)  # shape T,B,
+        data['done'] = torch.cat(data['done'], dim=0).reshape(self._unroll_len, -1).float()  # shape T,B,
+        data['reward'] = torch.cat(data['reward'], dim=0).reshape(self._unroll_len, -1)  # shape T,B,
+        data['weight'] = torch.cat(
+            data['weight'], dim=0
+        ).reshape(self._unroll_len, -1) if data['weight'] else None  # shape T,B
+        return data
+
+    def _forward_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]:
+        r"""
+        Overview:
+            Forward computation graph of learn mode(updating policy).
+        Arguments:
+            - data (:obj:`List[Dict[str, Any]]`): List type data, a list of data for training. Each list element is a \
+            dict, whose values are torch.Tensor or np.ndarray or dict/list combinations, keys include at least 'obs',\
+             'next_obs', 'logit', 'action', 'reward', 'done'
+        Returns:
+            - info_dict (:obj:`Dict[str, Any]`): Dict type data, a info dict indicated training result, which will be \
+                recorded in text log and tensorboard, values are python scalar or a list of scalars.
+        ArgumentsKeys:
+            - necessary: ``obs``, ``action``, ``reward``, ``next_obs``, ``done``
+            - optional: 'collect_iter', 'replay_unique_id', 'replay_buffer_idx', 'priority', 'staleness', 'use', 'IS'
+        ReturnsKeys:
+            - necessary: ``cur_lr_actor``, ``cur_lr_critic``, ``actor_loss`,``bc_loss``,``policy_loss``,\
+                ``critic_loss``,``entropy_loss``
+        """
+        data = self._data_preprocess_learn(data)
+        self._learn_model.train()
+        action_data = self._learn_model.forward(data['obs_plus_1'], mode='compute_actor')
+        q_value_data = self._learn_model.forward(data['obs_plus_1'], mode='compute_critic')
+        avg_action_data = self._target_model.forward(data['obs_plus_1'], mode='compute_actor')
+
+        target_logit, behaviour_logit, avg_logit, actions, q_values, rewards, weights = self._reshape_data(
+            action_data, avg_action_data, q_value_data, data
+        )
+        # shape (T+1),B,env_action_shape
+        target_pi = torch.softmax(target_logit, dim=-1)
+        # shape T,B,env_action_shape
+        behaviour_pi = torch.softmax(behaviour_logit, dim=-1)
+        # shape (T+1),B,env_action_shape
+        avg_pi = torch.softmax(avg_logit, dim=-1)
+        with torch.no_grad():
+            # shape T,B,env_action_shape
+            ratio = target_pi[0:-1, ...] / (behaviour_pi + EPS)
+            # shape (T+1),B,1
+            v_pred = (q_values * target_pi).sum(-1).unsqueeze(-1)
+            # Calculate retrace
+            q_retraces = compute_q_retraces(q_values, v_pred, rewards, actions, weights, ratio, self._gamma)
+
+        q_retraces = q_retraces[0:-1]  # shape T,B,1
+        q_values = q_values[0:-1]  # shape T,B,env_action_shape
+        v_pred = v_pred[0:-1]  # shape T,B,1
+        target_pi = target_pi[0:-1]  # shape T,B,env_action_shape
+        avg_pi = avg_pi[0:-1]  # shape T,B,env_action_shape
+        total_valid = weights.sum()  # 1
+        # ====================
+        # policy update
+        # ====================
+        actor_loss, bc_loss = acer_policy_error(
+            q_values, q_retraces, v_pred, target_pi, actions, ratio, self._c_clip_ratio
+        )
+        actor_loss = actor_loss * weights.unsqueeze(-1)
+        bc_loss = bc_loss * weights.unsqueeze(-1)
+        dist_new = torch.distributions.categorical.Categorical(probs=target_pi)
+        entropy_loss = (dist_new.entropy() * weights).unsqueeze(-1)  # shape T,B,1
+        total_actor_loss = (actor_loss + bc_loss + self._entropy_weight * entropy_loss).sum() / total_valid
+        self._optimizer_actor.zero_grad()
+        actor_gradients = torch.autograd.grad(-total_actor_loss, target_pi, retain_graph=True)
+        if self._use_trust_region:
+            actor_gradients = acer_trust_region_update(actor_gradients, target_pi, avg_pi, self._trust_region_value)
+        target_pi.backward(actor_gradients)
+        self._optimizer_actor.step()
+
+        # ====================
+        # critic update
+        # ====================
+        critic_loss = (acer_value_error(q_values, q_retraces, actions) * weights.unsqueeze(-1)).sum() / total_valid
+        self._optimizer_critic.zero_grad()
+        critic_loss.backward()
+        self._optimizer_critic.step()
+        self._target_model.update(self._learn_model.state_dict())
+
+        return {
+            'cur_actor_lr': self._optimizer_actor.defaults['lr'],
+            'cur_critic_lr': self._optimizer_critic.defaults['lr'],
+            'actor_loss': (actor_loss.sum() / total_valid).item(),
+            'bc_loss': (bc_loss.sum() / total_valid).item(),
+            'policy_loss': total_actor_loss.item(),
+            'critic_loss': critic_loss.item(),
+            'entropy_loss': (entropy_loss.sum() / total_valid).item(),
+        }
+
+    def _reshape_data(
+            self, action_data: Dict[str, Any], avg_action_data: Dict[str, Any], q_value_data: Dict[str, Any],
+            data: Dict[str, Any]
+    ) -> Tuple[Any, Any, Any, Any, Any, Any]:
+        r"""
+        Overview:
+            Obtain weights for loss calculating, where should be 0 for done positions
+            Update values and rewards with the weight
+        Arguments:
+            - output (:obj:`Dict[int, Any]`): Dict type data, output of learn_model forward. \
+             Values are torch.Tensor or np.ndarray or dict/list combinations,keys are value, logit.
+            - data (:obj:`Dict[int, Any]`): Dict type data, input of policy._forward_learn \
+             Values are torch.Tensor or np.ndarray or dict/list combinations. Keys includes at \
+             least ['logit', 'action', 'reward', 'done',]
+        Returns:
+            - data (:obj:`Tuple[Any]`): Tuple of target_logit, behaviour_logit, actions, \
+             values, rewards, weights
+        ReturnsShapes:
+            - target_logit (:obj:`torch.FloatTensor`): :math:`((T+1), B, Obs_Shape)`, where T is timestep,\
+             B is batch size and Obs_Shape is the shape of single env observation.
+            - behaviour_logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim.
+            - avg_action_logit (:obj:`torch.FloatTensor`): :math: `(T+1, B, N)`, where N is action dim.
+            - actions (:obj:`torch.LongTensor`): :math:`(T, B)`
+            - values (:obj:`torch.FloatTensor`): :math:`(T+1, B)`
+            - rewards (:obj:`torch.FloatTensor`): :math:`(T, B)`
+            - weights (:obj:`torch.FloatTensor`): :math:`(T, B)`
+        """
+        target_logit = action_data['logit'].reshape(
+            self._unroll_len + 1, -1, self._action_shape
+        )  # shape (T+1),B,env_action_shape
+        behaviour_logit = data['logit']  # shape T,B,env_action_shape
+        avg_action_logit = avg_action_data['logit'].reshape(
+            self._unroll_len + 1, -1, self._action_shape
+        )  # shape (T+1),B,env_action_shape
+        actions = data['action']  # shape T,B
+        values = q_value_data['q_value'].reshape(
+            self._unroll_len + 1, -1, self._action_shape
+        )  # shape (T+1),B,env_action_shape
+        rewards = data['reward']  # shape T,B
+        weights_ = 1 - data['done']  # shape T,B
+        weights = torch.ones_like(rewards)  # shape T,B
+        weights[1:, ...] = weights_[:-1, ...]
+        rewards = rewards * weights
+        # weights= weights_
+        # rewards = rewards  # shape T,B
+        return target_logit, behaviour_logit, avg_action_logit, actions, values, rewards, weights
+
+    def _state_dict_learn(self) -> Dict[str, Any]:
+        r"""
+        Overview:
+            Return the state_dict of learn mode, usually including model and optimizer.
+        Returns:
+            - state_dict (:obj:`Dict[str, Any]`): the dict of current policy learn state, for saving and restoring.
+        """
+        return {
+            'model': self._learn_model.state_dict(),
+            'actor_optimizer': self._optimizer_actor.state_dict(),
+            'critic_optimizer': self._optimizer_critic.state_dict(),
+        }
+
+    def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None:
+        r"""
+        Overview:
+            Load the state_dict variable into policy learn mode.
+        Arguments:
+            - state_dict (:obj:`Dict[str, Any]`): the dict of policy learn state saved before.
+        .. tip::
+            If you want to only load some parts of model, you can simply set the ``strict`` argument in \
+            load_state_dict to ``False``, or refer to ``ding.torch_utils.checkpoint_helper`` for more \
+            complicated operation.
+        """
+        self._learn_model.load_state_dict(state_dict['model'])
+        self._optimizer_actor.load_state_dict(state_dict['actor_optimizer'])
+        self._optimizer_critic.load_state_dict(state_dict['critic_optimizer'])
+
+    def _init_collect(self) -> None:
+        r"""
+        Overview:
+            Collect mode init method. Called by ``self.__init__``, initialize algorithm arguments and collect_model.
+            Use multinomial_sample to choose action.
+        """
+        self._collect_unroll_len = self._cfg.collect.unroll_len
+        self._collect_model = model_wrap(self._model, wrapper_name='multinomial_sample')
+        self._collect_model.reset()
+
+    def _forward_collect(self, data: Dict[int, Any]) -> Dict[int, Dict[str, Any]]:
+        r"""
+        Overview:
+            Forward computation graph of collect mode(collect training data).
+        Arguments:
+            - data (:obj:`Dict[int, Any]`): Dict type data, stacked env data for predicting \
+            action, values are torch.Tensor or np.ndarray or dict/list combinations,keys \
+            are env_id indicated by integer.
+        Returns:
+            - output (:obj:`Dict[int, Dict[str,Any]]`): Dict of predicting policy_output(logit, action) for each env.
+        ReturnsKeys
+            - necessary: ``logit``, ``action``
+        """
+        data_id = list(data.keys())
+        data = default_collate(list(data.values()))
+        if self._cuda:
+            data = to_device(data, self._device)
+        self._collect_model.eval()
+        with torch.no_grad():
+            output = self._collect_model.forward(data, mode='compute_actor')
+        if self._cuda:
+            output = to_device(output, 'cpu')
+        output = default_decollate(output)
+        output = {i: d for i, d in zip(data_id, output)}
+        return output
+
+    def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        r"""
+        Overview:
+            For a given trajectory(transitions, a list of transition) data, process it into a list of sample that \
+            can be used for training directly.
+        Arguments:
+            - data (:obj:`List[Dict[str, Any]`): The trajectory data(a list of transition), each element is the same \
+                format as the return value of ``self._process_transition`` method.
+        Returns:
+            - samples (:obj:`dict`): List of training samples.
+        .. note::
+            We will vectorize ``process_transition`` and ``get_train_sample`` method in the following release version. \
+            And the user can customize the this data processing procedure by overriding this two methods and collector \
+            itself.
+        """
+        return get_train_sample(data, self._unroll_len)
+
+    def _process_transition(self, obs: Any, policy_output: Dict[str, Any], timestep: namedtuple) -> Dict[str, Any]:
+        r"""
+        Overview:
+               Generate dict type transition data from inputs.
+        Arguments:
+                - obs (:obj:`Any`): Env observation,can be torch.Tensor or np.ndarray or dict/list combinations.
+                - model_output (:obj:`dict`): Output of collect model, including ['logit','action']
+                - timestep (:obj:`namedtuple`): Output after env step, including at least ['obs', 'reward', 'done']\
+                       (here 'obs' indicates obs after env step).
+        Returns:
+               - transition (:obj:`dict`): Dict type transition data, including at least ['obs','next_obs', 'logit',\
+               'action','reward', 'done']
+        """
+        transition = {
+            'obs': obs,
+            'next_obs': timestep.obs,
+            'logit': policy_output['logit'],
+            'action': policy_output['action'],
+            'reward': timestep.reward,
+            'done': timestep.done,
+        }
+        return transition
+
+    def _init_eval(self) -> None:
+        r"""
+        Overview:
+            Evaluate mode init method. Called by ``self.__init__``, initialize eval_model,
+            and use argmax_sample to choose action.
+        """
+        self._eval_model = model_wrap(self._model, wrapper_name='argmax_sample')
+        self._eval_model.reset()
+
+    def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]:
+        r"""
+        Overview:
+            Forward computation graph of eval mode(evaluate policy performance), at most cases, it is similar to \
+            ``self._forward_collect``.
+        Arguments:
+            - data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \
+                values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer.
+        Returns:
+            - output (:obj:`Dict[int, Any]`): The dict of predicting action for the interaction with env.
+        ReturnsKeys
+            - necessary: ``action``
+            - optional: ``logit``
+
+        """
+        data_id = list(data.keys())
+        data = default_collate(list(data.values()))
+        if self._cuda:
+            data = to_device(data, self._device)
+        self._eval_model.eval()
+        with torch.no_grad():
+            output = self._eval_model.forward(data, mode='compute_actor')
+        if self._cuda:
+            output = to_device(output, 'cpu')
+        output = default_decollate(output)
+        output = {i: d for i, d in zip(data_id, output)}
+        return output
+
+    def default_model(self) -> Tuple[str, List[str]]:
+        return 'acer', ['ding.model.template.acer']
+
+    def _monitor_vars_learn(self) -> List[str]:
+        r"""
+        Overview:
+            Return this algorithm default model setting for demonstration.
+        Returns:
+            - model_info (:obj:`Tuple[str, List[str]]`): model name and mode import_names
+        .. note::
+            The user can define and use customized network model but must obey the same interface definition indicated \
+            by import_names path. For IMPALA, ``ding.model.interface.IMPALA``
+        """
+        return ['actor_loss', 'bc_loss', 'policy_loss', 'critic_loss', 'entropy_loss']

+
+ +
+ +
+
+ + +
+ +
+

+ © Copyright 2021, OpenDILab Contributors + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + \ No newline at end of file diff --git a/_modules/index.html b/_modules/index.html index 41f8832e8c71aa2ab40dd18925d51ec230fed91b..24513b32046fef8e093b2532657479c21ca1caba 100644 --- a/_modules/index.html +++ b/_modules/index.html @@ -186,6 +186,7 @@
  • ding.model.template.qmix
  • ding.model.template.vac
  • ding.policy.a2c
    • +
  • ding.policy.acer
  • ding.policy.atoc
  • ding.policy.c51
  • ding.policy.collaq
    • diff --git a/_sources/hands_on/acer.rst.txt b/_sources/hands_on/acer.rst.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac314f4ac39052582dbb0b70653a88ac1e02ed00 --- /dev/null +++ b/_sources/hands_on/acer.rst.txt @@ -0,0 +1,222 @@ +ACER +^^^^^^^ + +Overview +--------- +ACER, short for actor-critic with experience replay, is an off-policy actor-critic model with experience replay. It greatly increases +the sample efficiency and decreasing the data correlation. ACER uses retrace Q-value estimation, an efficient TRPO and truncates importance sample weights with +bias correction to control the stability of the off-policy estimator. You can find more details in this paper +`Sample Efficient Actor-Critic with Experience Replay `_. + + +Quick Facts +------------- +1. ACER is a **model-free** and **off-policy** RL algorithm. + +2. ACER can support both **discrete** action spaces and **continuous** action spaces with several differences +s +3. ACER is a actor-critic RL algorithm, which optimizes actor network and critic network, respectively. + +4. ACER decouples acting from learning. Collectors in ACER needs to record behavior probabilty distributions. + + +Key Equations +--------------------------- +Loss used in ACER contains policy loss and value loss. They often update seperately, so it's necessary to control their relative update speed. + +Retrace Q-value estimation +>>>>>>>>>>>>>>>>>>>>>>>>>>>> +Given a trajectory generated under the behavior policy :math:`\mu`, we retrieve a trajectory :math:`\{x_0, a_0, r_0, \mu(\cdot|x_0),..., x_k, a_k, r_k, \mu(\cdot|x_k)}` +the Retrace estimator can be +expressed recursively as follows: + +.. math:: + + Q^{\text{ret}}(x_t,a_t)=r_t+\gamma\bar{\rho}_{t+1}[Q^{\text{ret}}(x_{t+ + 1},a_{t+1})]+\gamma V(x_{t+1}) + +where :math:`\bar{\rho}_t` is the truncated importance weight, :math:`\bar{\rho}_t=\min\{c,\rho\}` with :math:`\frac{\pi(a_t|x_t)}{\mu(a_t|x_t)}`. :math:`\pi` is target policy +Retrace is an off-policy, return based algorithm which has low variance and is proven to converge to the value function of the taget policy for any behavior policy +we approximate the Q value by neural network :math:`Q_{\theta}` in a mean squared error loss: + + .. math:: + L_{\text{value}}=\frac{1}{2}(Q^{\text{ret}}(x_t,a_t)-Q_{\theta}(x_t,a_t))^2. + + +policy gradient +>>>>>>>>>>>>>>>>>>>>>> + +To safe-guard against high variance, ACER uses truncated importance weights and introduaces +correction term via the following decomposition of :math:`g^{acer}`: + +.. math:: + g^{\text{acer}}=\bar{\rho_t}\nabla_\theta\log\pi_{\theta}(a_t|x_t)[Q^{\text{ret}}(x_t,a_t)-V_{\theta}(x_t)]+\mathbb{E}_{a\sim \pi}\left([\frac{\rho_t(a)-c}{\rho_t(a)}]_+\nabla_{\theta}\log\pi_{\theta}(a|x_t)[Q_\theta(x_t,a)-V_{\theta}(x_t)\right)\right]. + +To ensure more stability, ACER limit the per-step change to the policy by solving the following linearized KL divergence constraint: + +.. math:: + \begin{split} + &\text{minimize}_z\quad\frac{1}{2}\|g_t^{\text{acer}}-z\|_2^2\\ + &subjec\ to\quad \nabla_{\phi_{\theta}(x_t)}D_{KL}[f(\cdot|\phi_{\theta_a}(x_t))\|f(\cdot|\phi_{\theta}(x_t))]^\top\le\delta + \end{split} + +The :math:`\phi(\theta)` is target policy network and the :math:`\phi(\theta_a)` is average policy network. +letting :math:`k=\nabla_{\phi_{\theta}(x_t)}D_{KL}[f(\cdot|\phi_{\theta_a}(x_t))\|f(\cdot|\phi_{\theta}(x_t))]`, the solution can be easily derived in closed form using the KKT condition: + +.. math:: + z^*=g_{t}^{\text{acer}}-\max\{0,\frac{k^\top g_t^{\text{acer}}-\delta}{\|k\|_2^2}\}k + + + + +Pseudocode +---------- + +There are several differences that using ACER on discrete action space or continuous action space. + +.. image:: images/ACER_alg1.png + :align: center + :scale: 70% + +.. image:: images/ACER_alg2.png + :align: center + :scale: 70% + +In continuous action space, it is impossible to enumerate all actions q value. So ACER uses sample actions to replace the expectation. + +.. image:: images/ACER_alg3.png + :align: center + :scale: 70% + + +Implementations +---------------- +Here we show the ACER algorithm on discrete action space. +The default config is defined as follows: + +.. autoclass:: ding.policy.acer.ACERPolicy + +Usually, we hope to compute everything as a batch to improve efficiency. This is done in ``policy._get_train_sample``. +Once we execute this function in collector, the length of samples will equal to unroll-len in config. For details, please +refer to doc of ``ding.rl_utils.adder``. + +You can find more information in :ref:`here ` + +The whole code of ACER you can find `here `_. +Here we show some details of this algorithm. + +First, we use the following functions to compute retrace Q value. + +.. code:: python + + def compute_q_retraces(q_values,v_pred,rewards,actions,weights,ratio,gamma=0.9): + """ + Overview: + Get Retrace Q value + Arguments: + - q_values (:obj:`torch.Tensor`): Q values + - v_pred (:obj:`torch.Tensor`): V values + - reward (:obj:`torch.Tensor`): reward values + - actions (:obj:`torch.Tensor`): The actions in replay buffer + - weights (:obj:`torch.Tensor`): setting padding postion + - ratio (:obj:`torch.Tensor`): ratio of new polcy with behavior policy + Returns: + - q_retraces (:obj:`torch.Tensor`): retrace Q values + """ + rewards = rewards.unsqueeze(-1) + actions = actions.unsqueeze(-1) + weights = weights.unsqueeze(-1) + q_retraces = torch.zeros_like(v_pred) + n_len = q_retraces.size()[0] + tmp_retraces = v_pred[-1,...] + q_retraces[-1,...] = v_pred[-1,...] + q_gather = torch.zeros_like(v_pred) + q_gather[0:-1,...] = q_values[0:-1,...].gather(-1,actions) + ratio_gather = ratio.gather(-1,actions) + for idx in reversed(range(n_len-1)): + q_retraces[idx,...] = rewards[idx,...]+gamma*weights[idx,...]*tmp_retraces + tmp_retraces = ratio_gather[idx,...].clamp(max=1.0)*(q_retraces[idx,...]-q_gather[idx,...])+v_pred[idx,...] + return q_retraces + + +After that, we calculate policy loss value, it will calcuate the actor loss with importance weights trunction and bias correction loss by the following function + +.. code:: python + + def acer_policy_error(q_values,q_retraces,v_pred,target_pi,actions,ratio,c_clip_ratio=10.0): + """ + Overview: + Get ACER policy loss + Arguments: + - q_values (:obj:`torch.Tensor`): Q values + - q_retraces (:obj:`torch.Tensor`): Q values (be calculated by retrace method) + - v_pred (:obj:`torch.Tensor`): V values + - target_pi (:obj:`torch.Tensor`): The new policy's probability + - actions (:obj:`torch.Tensor`): The actions in replay buffer + - ratio (:obj:`torch.Tensor`): ratio of new polcy with behavior policy + - c_clip_ratio (:obj:`float`): clip value for ratio + Returns: + - actor_loss (:obj:`torch.Tensor`): policy loss from q_retrace + - bc_loss (:obj:`torch.Tensor`): bias correct policy loss + """ + actions=actions.unsqueeze(-1) + with torch.no_grad(): + advantage_retraces = q_retraces-v_pred + advantage_native = q_values-v_pred + actor_loss = ratio.gather(-1,actions).clamp(max=c_clip_ratio)*advantage_retraces*(target_pi.gather(-1,actions)+EPS).log() + bc_loss = (1.0-c_clip_ratio/(ratio+EPS)).clamp(min=0.0)*target_pi.detach()*advantage_native*(target_pi+EPS).log() + bc_loss=bc_loss.sum(-1).unsqueeze(-1) + return actor_loss,bc_loss + +Then, we execute backward operation towards target_pi. Moreover, we need to calculate the correction gradient in the trust region: + +.. code:: python + + def acer_trust_region_update(actor_gradients,target_pi,avg_pi,trust_region_value): + """ + Overview: + calcuate gradient with trust region constrain + Arguments: + - actor_gradients (:obj:`list(torch.Tensor)`): gradients value's for different part + - target_pi (:obj:`torch.Tensor`): The new policy's probability + - avg_pi (:obj:`torch.Tensor`): The average policy's probability + - trust_region_value (:obj:`float`): the range of trust region + Returns: + - update_gradients (:obj:`torch.Tensor`): gradients under trust region constraint + """ + with torch.no_grad(): + KL_gradients = [(avg_pi/(target_pi+EPS))] + update_gradients = [] + for actor_gradient,KL_gradient in zip(actor_gradients,KL_gradients): + scale = actor_gradient.mul(KL_gradient).sum(-1).unsqueeze(-1)-trust_region_value + scale = torch.div(scale,KL_gradient.mul(KL_gradient).sum(-1).unsqueeze(-1)).clamp(min=0.0) + update_gradients.append(actor_gradient-scale*KL_gradient) + return update_gradients + +With new gradients, we can continue to backward and then update parameters with gradients. + +Finally, we should calculate the Q value loss to update Q-Network + +.. code:: python + + def acer_value_error(q_values,q_retraces,actions): + """ + Overview: + Get ACER critic loss + Arguments: + - q_values (:obj:`torch.Tensor`): Q values + - q_retraces (:obj:`torch.Tensor`): Q values (be calculated by retrace method) + - actions (:obj:`torch.Tensor`): The actions in replay buffer + - ratio (:obj:`torch.Tensor`): ratio of new polcy with behavior policy + Returns: + - critic_loss (:obj:`torch.Tensor`): critic loss + """ + actions=actions.unsqueeze(-1) + critic_loss=0.5*(q_retraces-q_values.gather(-1,actions)).pow(2) + return critic_loss + + + +Reference +---------- +Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, Nando de Freitas: "Sample Efficient Actor-Critic with Experience Replay", 2016; [https://arxiv.org/abs/1611.01224 arxiv:1611.01224]. diff --git a/_sources/hands_on/impala.rst.txt b/_sources/hands_on/impala.rst.txt index c868f699525ab6b8ff4bf6268620adb7ea0d215f..205d911353839a02d322a73470fac65d4b5dbe6b 100644 --- a/_sources/hands_on/impala.rst.txt +++ b/_sources/hands_on/impala.rst.txt @@ -120,6 +120,7 @@ need all training sample (sequences of training data) have the same length. This Once we execute this function in collector, the length of samples will equal to unroll-len in config. For details, please refer to doc of ``ding.rl_utils.adder``. +.. _ref2other: .. code:: python def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: diff --git a/api_doc/league/player.html b/api_doc/league/player.html index 00742d2234cacfb04c9c2771f5c37bbf90364d00..7173f74fa7b67290aa4a79fabd35f598ec02b9ad 100644 --- a/api_doc/league/player.html +++ b/api_doc/league/player.html @@ -199,7 +199,7 @@

    Player

    -class ding.league.player.Player(cfg: easydict.EasyDict, category: str, init_payoff: BattleSharedPayoff, checkpoint_path: str, player_id: str, total_agent_step: int)[source]
    +class ding.league.player.Player(cfg: easydict.EasyDict, category: str, init_payoff: BattleSharedPayoff, checkpoint_path: str, player_id: str, total_agent_step: int, rating: PlayerRating)[source]
    Overview:

    Base player class, player is the basic member of a league

    @@ -210,7 +210,7 @@
    -__init__(cfg: easydict.EasyDict, category: str, init_payoff: BattleSharedPayoff, checkpoint_path: str, player_id: str, total_agent_step: int) → None[source]
    +__init__(cfg: easydict.EasyDict, category: str, init_payoff: BattleSharedPayoff, checkpoint_path: str, player_id: str, total_agent_step: int, rating: PlayerRating) → None[source]
    Overview:

    Initialize base player metadata

    @@ -221,6 +221,7 @@

  • checkpoint_path (str): The path to load player checkpoint.

  • player_id (str): Player id in string format.

  • total_agent_step (int): For active player, it should be 0; For historical player, it should be parent player’s _total_agent_step when snapshot.

    • +

  • rating (PlayerRating): player rating information in total league

    • @@ -387,10 +388,14 @@ If yes, set self._l
    -snapshot() → ding.league.player.HistoricalPlayer
    +snapshot(metric_env: LeagueMetricEnv) → ding.league.player.HistoricalPlayer
    Overview:

    Generate a snapshot historical player from the current player, called in league’s _snapshot.

    +
    Argument:
      +

    • metric_env (LeagueMetricEnv): player rating environment, one league one env

      • +
    +
    Returns:
    @@ -487,10 +492,14 @@ If yes, set self._l
    -snapshot() → ding.league.player.HistoricalPlayer
    +snapshot(metric_env: LeagueMetricEnv) → ding.league.player.HistoricalPlayer
    Overview:

    Generate a snapshot historical player from the current player, called in league’s _snapshot.

    +
    Argument:
      +

    • metric_env (LeagueMetricEnv): player rating environment, one league one env

      • +
    +
    Returns:

    • snapshot_player (HistoricalPlayer): new instantiated historical player

    @@ -575,10 +584,14 @@ If yes, set self._l
    -snapshot() → ding.league.player.HistoricalPlayer
    +snapshot(metric_env: LeagueMetricEnv) → ding.league.player.HistoricalPlayer
    Overview:

    Generate a snapshot historical player from the current player, called in league’s _snapshot.

    +
    Argument:
      +

    • metric_env (LeagueMetricEnv): player rating environment, one league one env

      • +
    +
    Returns:

    • snapshot_player (HistoricalPlayer): new instantiated historical player

    @@ -662,10 +675,14 @@ If yes, set self._l
    -snapshot() → ding.league.player.HistoricalPlayer
    +snapshot(metric_env: LeagueMetricEnv) → ding.league.player.HistoricalPlayer
    Overview:

    Generate a snapshot historical player from the current player, called in league’s _snapshot.

    +
    Argument:
      +

    • metric_env (LeagueMetricEnv): player rating environment, one league one env

      • +
    +
    Returns:

    • snapshot_player (HistoricalPlayer): new instantiated historical player

    diff --git a/genindex.html b/genindex.html index ccc92c4f99484793a26ae8baeaed28709aab49e3..f7e5e31e48ce7421f1bc453d0c4d88f0bb765c1f 100644 --- a/genindex.html +++ b/genindex.html @@ -823,6 +823,8 @@
      +
    • add_used_data() (ding.worker.replay_buffer.utils.UsedDataRemover method) +
    • Adder (class in ding.rl_utils.adder)
    • AdvancedReplayBuffer (class in ding.worker.replay_buffer.advanced_buffer) diff --git a/hands_on/acer.html b/hands_on/acer.html new file mode 100644 index 0000000000000000000000000000000000000000..617db2481924f9f1b6e291e7b3ebcd19fb3f8ace --- /dev/null +++ b/hands_on/acer.html @@ -0,0 +1,533 @@ + + + + + + + + + + + ACER — DI-engine 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + + +
      + + + + + +
      + +
      + + + + + + + + + + + + + + + + + +
      + + + + +
      +
      +
      +
      + +
      +

      ACER

      +
      +

      Overview

      +

      ACER, short for actor-critic with experience replay, is an off-policy actor-critic model with experience replay. It greatly increases +the sample efficiency and decreasing the data correlation. ACER uses retrace Q-value estimation, an efficient TRPO and truncates importance sample weights with +bias correction to control the stability of the off-policy estimator. You can find more details in this paper +Sample Efficient Actor-Critic with Experience Replay.

      +
      +
      +

      Quick Facts

      +
        +

      1. ACER is a model-free and off-policy RL algorithm.

        2. +
      +

      2. ACER can support both discrete action spaces and continuous action spaces with several differences +s +3. ACER is a actor-critic RL algorithm, which optimizes actor network and critic network, respectively.

      +
        +

      1. ACER decouples acting from learning. Collectors in ACER needs to record behavior probabilty distributions.

        2. +
      +
      +
      +

      Key Equations

      +

      Loss used in ACER contains policy loss and value loss. They often update seperately, so it’s necessary to control their relative update speed.

      +
      +

      Retrace Q-value estimation

      +

      Given a trajectory generated under the behavior policy \(\mu\), we retrieve a trajectory \(\{x_0, a_0, r_0, \mu(\cdot|x_0),..., x_k, a_k, r_k, \mu(\cdot|x_k)}\) +the Retrace estimator can be +expressed recursively as follows:

      +
      +\[Q^{\text{ret}}(x_t,a_t)=r_t+\gamma\bar{\rho}_{t+1}[Q^{\text{ret}}(x_{t+ +1},a_{t+1})]+\gamma V(x_{t+1})\]
      +

      where \(\bar{\rho}_t\) is the truncated importance weight, \(\bar{\rho}_t=\min\{c,\rho\}\) with \(\frac{\pi(a_t|x_t)}{\mu(a_t|x_t)}\). \(\pi\) is target policy +Retrace is an off-policy, return based algorithm which has low variance and is proven to converge to the value function of the taget policy for any behavior policy +we approximate the Q value by neural network \(Q_{\theta}\) in a mean squared error loss:

      +
      +
      +\[L_{\text{value}}=\frac{1}{2}(Q^{\text{ret}}(x_t,a_t)-Q_{\theta}(x_t,a_t))^2.\]
      +
      +
      +
      +

      policy gradient

      +

      To safe-guard against high variance, ACER uses truncated importance weights and introduaces +correction term via the following decomposition of \(g^{acer}\):

      +
      +\[g^{\text{acer}}=\bar{\rho_t}\nabla_\theta\log\pi_{\theta}(a_t|x_t)[Q^{\text{ret}}(x_t,a_t)-V_{\theta}(x_t)]+\mathbb{E}_{a\sim \pi}\left([\frac{\rho_t(a)-c}{\rho_t(a)}]_+\nabla_{\theta}\log\pi_{\theta}(a|x_t)[Q_\theta(x_t,a)-V_{\theta}(x_t)\right)\right].\]
      +

      To ensure more stability, ACER limit the per-step change to the policy by solving the following linearized KL divergence constraint:

      +
      +\[\begin{split}\begin{split} +&\text{minimize}_z\quad\frac{1}{2}\|g_t^{\text{acer}}-z\|_2^2\\ +&subjec\ to\quad \nabla_{\phi_{\theta}(x_t)}D_{KL}[f(\cdot|\phi_{\theta_a}(x_t))\|f(\cdot|\phi_{\theta}(x_t))]^\top\le\delta +\end{split}\end{split}\]
      +

      The \(\phi(\theta)\) is target policy network and the \(\phi(\theta_a)\) is average policy network. +letting \(k=\nabla_{\phi_{\theta}(x_t)}D_{KL}[f(\cdot|\phi_{\theta_a}(x_t))\|f(\cdot|\phi_{\theta}(x_t))]\), the solution can be easily derived in closed form using the KKT condition:

      +
      +\[z^*=g_{t}^{\text{acer}}-\max\{0,\frac{k^\top g_t^{\text{acer}}-\delta}{\|k\|_2^2}\}k\]
      +
      +
      +
      +

      Pseudocode

      +

      There are several differences that using ACER on discrete action space or continuous action space.

      +../_images/ACER_alg1.png +../_images/ACER_alg2.png +

      In continuous action space, it is impossible to enumerate all actions q value. So ACER uses sample actions to replace the expectation.

      +../_images/ACER_alg3.png +
      +
      +

      Implementations

      +

      Here we show the ACER algorithm on discrete action space. +The default config is defined as follows:

      +
      +
      +class ding.policy.acer.ACERPolicy(cfg: dict, model: Optional[Union[type, torch.nn.modules.module.Module]] = None, enable_field: Optional[List[str]] = None)[source]
      +
      +
      Overview:

      Policy class of ACER algorithm.

      +
      +
      Config:
      ++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      ID

      Symbol

      Type

      Default Value

      Description

      Other(Shape)

      1

      type

      str

      acer

      +
      RL policy register name, refer to
      +
      registry POLICY_REGISTRY
      +
      +
      +
      this arg is optional,
      +
      a placeholder
      +
      +

      2

      cuda

      bool

      False

      +
      Whether to use cuda for network
      +

      +
      +
      +
      this arg can be diff-
      +
      erent from modes
      +
      +

      3

      on_policy

      bool

      False

      +
      Whether the RL algorithm is
      +
      on-policy or off-policy
      +
      +

      4

      trust_region

      bool

      True

      +
      Whether the RL algorithm use trust
      +
      region constraint
      +
      +
      +

      +

      +
      +

      5

      trust_region_value

      float

      1.0

      +
      maximum range of the trust region
      +
      +
      +

      +
      +

      6

      unroll_len

      int

      32

      +
      trajectory length to calculate
      +
      Q retrace target
      +
      +

      7

      learn.update +per_collect

      int

      4

      +
      How many updates(iterations) to
      +
      train after collector’s one
      +
      +
      collection. Only
      +
      +
      valid in serial training
      +
      +
      +
      this args can be vary
      +
      from envs. Bigger val
      +

      +
      means more off-policy
      +
      +

      8

      c_clip_ratio

      float

      1.0

      +
      clip ratio of importance weights
      +
      +
      +

      +
      +
      +
      +
      +
      + +

      Usually, we hope to compute everything as a batch to improve efficiency. This is done in policy._get_train_sample. +Once we execute this function in collector, the length of samples will equal to unroll-len in config. For details, please +refer to doc of ding.rl_utils.adder.

      +

      You can find more information in here

      +

      The whole code of ACER you can find here. +Here we show some details of this algorithm.

      +

      First, we use the following functions to compute retrace Q value.

      +
      def compute_q_retraces(q_values,v_pred,rewards,actions,weights,ratio,gamma=0.9):
+    """
+        Overview:
+                Get Retrace Q value
+            Arguments:
+                - q_values (:obj:`torch.Tensor`): Q values
+                - v_pred (:obj:`torch.Tensor`): V values
+                - reward (:obj:`torch.Tensor`): reward values
+                - actions (:obj:`torch.Tensor`): The actions in replay buffer
+                - weights (:obj:`torch.Tensor`): setting padding postion
+                - ratio (:obj:`torch.Tensor`): ratio of new polcy with behavior policy
+            Returns:
+                - q_retraces (:obj:`torch.Tensor`):  retrace Q values
+    """
+    rewards = rewards.unsqueeze(-1)
+    actions = actions.unsqueeze(-1)
+    weights = weights.unsqueeze(-1)
+    q_retraces = torch.zeros_like(v_pred)
+    n_len = q_retraces.size()[0]
+    tmp_retraces = v_pred[-1,...]
+    q_retraces[-1,...] = v_pred[-1,...]
+    q_gather = torch.zeros_like(v_pred)
+    q_gather[0:-1,...] = q_values[0:-1,...].gather(-1,actions)
+    ratio_gather = ratio.gather(-1,actions)
+    for idx in reversed(range(n_len-1)):
+        q_retraces[idx,...] = rewards[idx,...]+gamma*weights[idx,...]*tmp_retraces
+        tmp_retraces = ratio_gather[idx,...].clamp(max=1.0)*(q_retraces[idx,...]-q_gather[idx,...])+v_pred[idx,...]
+    return q_retraces
+
      +
      +

      After that, we calculate policy loss value, it will calcuate the actor loss with importance weights trunction and bias correction loss by the following function

      +
      def acer_policy_error(q_values,q_retraces,v_pred,target_pi,actions,ratio,c_clip_ratio=10.0):
+    """
+        Overview:
+            Get ACER policy loss
+        Arguments:
+            - q_values (:obj:`torch.Tensor`): Q values
+            - q_retraces (:obj:`torch.Tensor`): Q values (be calculated by retrace method)
+            - v_pred (:obj:`torch.Tensor`): V values
+            - target_pi (:obj:`torch.Tensor`): The new policy's probability
+            - actions (:obj:`torch.Tensor`): The actions in replay buffer
+            - ratio (:obj:`torch.Tensor`): ratio of new polcy with behavior policy
+            - c_clip_ratio (:obj:`float`): clip value for ratio
+        Returns:
+            - actor_loss (:obj:`torch.Tensor`): policy loss from q_retrace
+            - bc_loss (:obj:`torch.Tensor`): bias correct policy loss
+    """
+    actions=actions.unsqueeze(-1)
+    with torch.no_grad():
+        advantage_retraces = q_retraces-v_pred
+        advantage_native = q_values-v_pred
+    actor_loss = ratio.gather(-1,actions).clamp(max=c_clip_ratio)*advantage_retraces*(target_pi.gather(-1,actions)+EPS).log()
+    bc_loss = (1.0-c_clip_ratio/(ratio+EPS)).clamp(min=0.0)*target_pi.detach()*advantage_native*(target_pi+EPS).log()
+    bc_loss=bc_loss.sum(-1).unsqueeze(-1)
+    return actor_loss,bc_loss
+
      +
      +

      Then, we execute backward operation towards target_pi. Moreover, we need to calculate the correction gradient in the trust region:

      +
      def acer_trust_region_update(actor_gradients,target_pi,avg_pi,trust_region_value):
+    """
+        Overview:
+            calcuate gradient with trust region constrain
+        Arguments:
+            - actor_gradients (:obj:`list(torch.Tensor)`): gradients value's for different part
+            - target_pi (:obj:`torch.Tensor`): The new policy's probability
+            - avg_pi (:obj:`torch.Tensor`): The average policy's probability
+            - trust_region_value (:obj:`float`): the range of trust region
+        Returns:
+            - update_gradients (:obj:`torch.Tensor`): gradients under trust region constraint
+        """
+    with torch.no_grad():
+        KL_gradients = [(avg_pi/(target_pi+EPS))]
+    update_gradients = []
+    for actor_gradient,KL_gradient in zip(actor_gradients,KL_gradients):
+        scale = actor_gradient.mul(KL_gradient).sum(-1).unsqueeze(-1)-trust_region_value
+        scale = torch.div(scale,KL_gradient.mul(KL_gradient).sum(-1).unsqueeze(-1)).clamp(min=0.0)
+        update_gradients.append(actor_gradient-scale*KL_gradient)
+    return update_gradients
+
      +
      +

      With new gradients, we can continue to backward and then update parameters with gradients.

      +

      Finally, we should calculate the Q value loss to update Q-Network

      +
      def acer_value_error(q_values,q_retraces,actions):
+    """
+        Overview:
+            Get ACER critic loss
+        Arguments:
+            - q_values (:obj:`torch.Tensor`): Q values
+            - q_retraces (:obj:`torch.Tensor`): Q values (be calculated by retrace method)
+            - actions (:obj:`torch.Tensor`): The actions in replay buffer
+            - ratio (:obj:`torch.Tensor`): ratio of new polcy with behavior policy
+        Returns:
+            - critic_loss (:obj:`torch.Tensor`): critic loss
+        """
+    actions=actions.unsqueeze(-1)
+    critic_loss=0.5*(q_retraces-q_values.gather(-1,actions)).pow(2)
+    return critic_loss
+
      +
      +
      +
      +

      Reference

      +

      Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, Nando de Freitas: “Sample Efficient Actor-Critic with Experience Replay”, 2016; [https://arxiv.org/abs/1611.01224 arxiv:1611.01224].

      +
      +
      + + +
      + +
      +
      + + +
      + +
      +

      + © Copyright 2021, OpenDILab Contributors + +

      +
      + Built with Sphinx using a theme provided by Read the Docs. + +
      + +
      +
      + +
      + +
      + + + + + + + + + + + + \ No newline at end of file diff --git a/hands_on/impala.html b/hands_on/impala.html index da5bd300fe28795de0ed59aab1ee444b2c54e42e..33ebc956d91abaadd73e77f7f2336b60ee32cd31 100644 --- a/hands_on/impala.html +++ b/hands_on/impala.html @@ -415,7 +415,7 @@ synchronize after each iteration.

      need all training sample (sequences of training data) have the same length. This is done in policy._get_train_sample. Once we execute this function in collector, the length of samples will equal to unroll-len in config. For details, please refer to doc of ding.rl_utils.adder.

      -
      def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+
      def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
     return get_train_sample(data, self._unroll_len)
 
 def get_train_sample(cls, data: List[Dict[str, Any]], unroll_len: int, last_fn_type: str = 'last') -> List[Dict[str, Any]]:
diff --git a/objects.inv b/objects.inv
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Binary files a/objects.inv and b/objects.inv differ
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Processes","Advantages","Loader Calculation Process","Loader Framework","Imitation Learning","Inverse RL","Customization 1: Dynamic Update Step","How to Customize an Env Wrapper","How to use Episode Replay Buffer?","Best Practice","Learner log","How to Customize Model Wrapper","How to use multiple buffers?","Multi-Discrete Example","How to Use Multi-GPU to Train Your Model","N-step TD","How to Use PER(Prioritized Experience Replay)","How to randomly collect some data sample at the beginning?","Random seed","Registry","How to use RNN","How to understand training generated folders?","FAQ","FAQ","Algorithm Overview","Autolog Overview","Collector Overview","Collector Overview","DataLoader Overview","Env Manager Overview","Env Manager Overview","Env Overview","Env Overview","HPC_RL Overview","DI-hpc Overview","Feature","Interaction Overview","Interaction Overview","League Overview","Learner Overview","Learner Overview","Loader Overview","Loader Overview","Model Overview","DI-orchestrator 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