# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import pickle from collections import deque from datetime import datetime import gym import numpy as np import paddle.fluid as fluid from tqdm import tqdm import parl from atari_agent import AtariAgent from atari_model import AtariModel from parl.utils import logger, summary from per_alg import PrioritizedDoubleDQN, PrioritizedDQN from proportional_per import ProportionalPER from utils import get_player MEMORY_SIZE = 1e6 MEMORY_WARMUP_SIZE = MEMORY_SIZE IMAGE_SIZE = (84, 84) CONTEXT_LEN = 4 FRAME_SKIP = 4 UPDATE_FREQ = 4 GAMMA = 0.99 LEARNING_RATE = 0.00025 / 4 def beta_adder(init_beta, step_size=0.0001): beta = init_beta step_size = step_size def adder(): nonlocal beta, step_size beta += step_size return min(beta, 1) return adder def process_transitions(transitions): transitions = np.array(transitions) batch_obs = np.stack(transitions[:, 0].copy()) batch_act = transitions[:, 1].copy() batch_reward = transitions[:, 2].copy() batch_next_obs = np.expand_dims(np.stack(transitions[:, 3]), axis=1) batch_next_obs = np.concatenate([batch_obs, batch_next_obs], axis=1)[:, 1:, :, :].copy() batch_terminal = transitions[:, 4].copy() batch = (batch_obs, batch_act, batch_reward, batch_next_obs, batch_terminal) return batch def run_episode(env, agent, per, mem=None, warmup=False, train=False): total_reward = 0 all_cost = [] traj = deque(maxlen=CONTEXT_LEN) obs = env.reset() for _ in range(CONTEXT_LEN - 1): traj.append(np.zeros(obs.shape)) steps = 0 if warmup: decay_exploration = False else: decay_exploration = True while True: steps += 1 traj.append(obs) context = np.stack(traj, axis=0) action = agent.sample(context, decay_exploration=decay_exploration) next_obs, reward, terminal, _ = env.step(action) transition = [obs, action, reward, next_obs, terminal] if warmup: mem.append(transition) if train: per.store(transition) if steps % UPDATE_FREQ == 0: beta = get_beta() transitions, idxs, sample_weights = per.sample(beta=beta) batch = process_transitions(transitions) cost, delta = agent.learn(*batch, sample_weights) all_cost.append(cost) per.update(idxs, delta) total_reward += reward obs = next_obs if terminal: break return total_reward, steps, np.mean(all_cost) def run_evaluate_episode(env, agent): obs = env.reset() total_reward = 0 while True: action = agent.predict(obs) obs, reward, isOver, info = env.step(action) total_reward += reward if isOver: break return total_reward def main(): # Prepare environments env = get_player( args.rom, image_size=IMAGE_SIZE, train=True, frame_skip=FRAME_SKIP) test_env = get_player( args.rom, image_size=IMAGE_SIZE, frame_skip=FRAME_SKIP, context_len=CONTEXT_LEN) # Init Prioritized Replay Memory per = ProportionalPER(alpha=0.6, seg_num=args.batch_size, size=MEMORY_SIZE) # Prepare PARL agent act_dim = env.action_space.n model = AtariModel(act_dim) if args.alg == 'ddqn': algorithm = PrioritizedDoubleDQN( model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE) elif args.alg == 'dqn': algorithm = PrioritizedDQN( model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE) agent = AtariAgent(algorithm, act_dim=act_dim, update_freq=UPDATE_FREQ) # Replay memory warmup total_step = 0 with tqdm(total=MEMORY_SIZE, desc='[Replay Memory Warm Up]') as pbar: mem = [] while total_step < MEMORY_WARMUP_SIZE: total_reward, steps, _ = run_episode( env, agent, per, mem=mem, warmup=True) total_step += steps pbar.update(steps) per.elements.from_list(mem[:int(MEMORY_WARMUP_SIZE)]) env_name = args.rom.split('/')[-1].split('.')[0] test_flag = 0 total_steps = 0 pbar = tqdm(total=args.train_total_steps) while total_steps < args.train_total_steps: # start epoch total_reward, steps, loss = run_episode(env, agent, per, train=True) total_steps += steps pbar.set_description('[train]exploration:{}'.format(agent.exploration)) summary.add_scalar('{}/score'.format(env_name), total_reward, total_steps) summary.add_scalar('{}/loss'.format(env_name), loss, total_steps) # mean of total loss summary.add_scalar('{}/exploration'.format(env_name), agent.exploration, total_steps) pbar.update(steps) if total_steps // args.test_every_steps >= test_flag: while total_steps // args.test_every_steps >= test_flag: test_flag += 1 pbar.write("testing") test_rewards = [] for _ in tqdm(range(3), desc='eval agent'): eval_reward = run_evaluate_episode(test_env, agent) test_rewards.append(eval_reward) eval_reward = np.mean(test_rewards) logger.info( "eval_agent done, (steps, eval_reward): ({}, {})".format( total_steps, eval_reward)) summary.add_scalar('{}/eval'.format(env_name), eval_reward, total_steps) pbar.close() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--rom', help='path of the rom of the atari game', required=True) parser.add_argument( '--batch_size', type=int, default=32, help='batch size for training') parser.add_argument( '--alg', type=str, default="ddqn", help='dqn or ddqn, training algorithm to use.') parser.add_argument( '--train_total_steps', type=int, default=int(1e7), help='maximum environmental steps of games') parser.add_argument( '--test_every_steps', type=int, default=100000, help='the step interval between two consecutive evaluations') args = parser.parse_args() assert args.alg in ['dqn','ddqn'], \ 'used algorithm should be dqn or ddqn (double dqn)' get_beta = beta_adder(init_beta=0.5) main()