From 6fdf4448d820e6f1b7fd194ae9664cf27206dc0a Mon Sep 17 00:00:00 2001 From: Hongsheng Zeng Date: Thu, 24 Jan 2019 20:11:32 +0800 Subject: [PATCH] Add more dqn benchmark result and unify train scripts (#46) * add more dqn benchmark result; unify train scripts * resize benchmark picture * resize benchmark picture, refine comments of args * change dependence, mujoco only support python3 now --- examples/DDPG/README.md | 6 ++-- examples/DDPG/train.py | 34 ++++++++++++++---- examples/DQN/.benchmark/DQN_BeamRider.png | Bin 0 -> 67713 bytes examples/DQN/.benchmark/DQN_Breakout.png | Bin 0 -> 75418 bytes examples/DQN/README.md | 6 ++-- examples/DQN/train.py | 42 ++++++++++++---------- examples/PPO/README.md | 4 +-- examples/PPO/train.py | 34 +++++++++++------- 8 files changed, 81 insertions(+), 45 deletions(-) create mode 100644 examples/DQN/.benchmark/DQN_BeamRider.png create mode 100644 examples/DQN/.benchmark/DQN_Breakout.png diff --git a/examples/DDPG/README.md b/examples/DDPG/README.md index f11a30a..d62d062 100644 --- a/examples/DDPG/README.md +++ b/examples/DDPG/README.md @@ -8,12 +8,12 @@ Based on PARL, the DDPG model of deep reinforcement learning is reproduced, and Please see [here](https://github.com/openai/mujoco-py) to know more about Mujoco game. ### Benchmark result -- HalfCheetah-v2 - + +DDPG_HalfCheetah-v2 ## How to use ### Dependencies: -+ python2.7 or python3.5+ ++ python3.5+ + [paddlepaddle>=1.0.0](https://github.com/PaddlePaddle/Paddle) + [parl](https://github.com/PaddlePaddle/PARL) + gym diff --git a/examples/DDPG/train.py b/examples/DDPG/train.py index c8f7ce3..ef02304 100644 --- a/examples/DDPG/train.py +++ b/examples/DDPG/train.py @@ -37,7 +37,9 @@ ENV_SEED = 1 def run_train_episode(env, agent, rpm): obs = env.reset() total_reward = 0 + steps = 0 while True: + steps += 1 batch_obs = np.expand_dims(obs, axis=0) action = agent.predict(batch_obs.astype('float32')) action = np.squeeze(action) @@ -62,7 +64,7 @@ def run_train_episode(env, agent, rpm): if done: break - return total_reward + return total_reward, steps def run_evaluate_episode(env, agent): @@ -105,18 +107,36 @@ def main(): rpm = ReplayMemory(MEMORY_SIZE, obs_dim, act_dim) - for i in range(MAX_EPISODES): - train_reward = run_train_episode(env, agent, rpm) - logger.info('Episode: {} Reward: {}'.format(i, train_reward)) - if (i + 1) % TEST_EVERY_EPISODES == 0: + test_flag = 0 + total_steps = 0 + while total_steps < args.train_total_steps: + train_reward, steps = run_train_episode(env, agent, rpm) + total_steps += steps + logger.info('Steps: {} Reward: {}'.format(total_steps, train_reward)) + + if total_steps // args.test_every_steps >= test_flag: + while total_steps // args.test_every_steps >= test_flag: + test_flag += 1 evaluate_reward = run_evaluate_episode(env, agent) - logger.info('Episode {}, Evaluate reward: {}'.format( - i, evaluate_reward)) + logger.info('Steps {}, Evaluate reward: {}'.format( + total_steps, evaluate_reward)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( '--env', help='Mujoco environment name', default='HalfCheetah-v2') + parser.add_argument( + '--train_total_steps', + type=int, + default=int(1e7), + help='maximum training steps') + parser.add_argument( + '--test_every_steps', + type=int, + default=int(1e4), + help='the step interval between two consecutive evaluations') + args = parser.parse_args() + main() diff --git a/examples/DQN/.benchmark/DQN_BeamRider.png b/examples/DQN/.benchmark/DQN_BeamRider.png new file mode 100644 index 0000000000000000000000000000000000000000..934d7aee81d45ee872fec27b60cdfacf7b366ed6 GIT binary patch literal 67713 zcmagFcQ{<#7e6{mgh-;3NR$yqCkUb^gXnDp(Oa}&v_V8og6M?NTZ|sPGink-)aYVN zc=Zyah0)!^`+k4--aqbhJQsBpr^ko?EH<_!lp$7;=PJQ_y7{Lad0{@giD84}Gy4WJT zE!}NE&Xx#QCl`d1gB7cnjk||~3!GJ0KuF*rAFDkA;VLO8_9=dEcY zXn^lv%K5ty@T&u;wEVEf)rDbpTxha?N8D5tzd8VC<*Y)2DPecOou<&RKG?Z;Dy5ay z9E1w0%zt?%E`kdBhaTF018f^isT53UP7BR2eD9U{AUN*KYv3>Ds2!F45oX4kKck~V 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+DQN_BeamRider ## How to use ### Dependencies: diff --git a/examples/DQN/train.py b/examples/DQN/train.py index 06cf8eb..669fac7 100644 --- a/examples/DQN/train.py +++ b/examples/DQN/train.py @@ -41,9 +41,9 @@ def run_train_episode(env, agent, rpm): total_reward = 0 all_cost = [] state = env.reset() - step = 0 + steps = 0 while True: - step += 1 + steps += 1 context = rpm.recent_state() context.append(state) context = np.stack(context, axis=0) @@ -52,7 +52,7 @@ def run_train_episode(env, agent, rpm): rpm.append(Experience(state, action, reward, isOver)) # start training if rpm.size() > MEMORY_WARMUP_SIZE: - if step % UPDATE_FREQ == 0: + if steps % UPDATE_FREQ == 0: batch_all_state, batch_action, batch_reward, batch_isOver = rpm.sample_batch( args.batch_size) batch_state = batch_all_state[:, :CONTEXT_LEN, :, :] @@ -67,7 +67,7 @@ def run_train_episode(env, agent, rpm): if all_cost: logger.info('[Train]total_reward: {}, mean_cost: {}'.format( total_reward, np.mean(all_cost))) - return total_reward, step + return total_reward, steps def run_evaluate_episode(env, agent): @@ -104,34 +104,33 @@ def main(): with tqdm(total=MEMORY_WARMUP_SIZE) as pbar: while rpm.size() < MEMORY_WARMUP_SIZE: - total_reward, step = run_train_episode(env, agent, rpm) - pbar.update(step) + total_reward, steps = run_train_episode(env, agent, rpm) + pbar.update(steps) # train test_flag = 0 - pbar = tqdm(total=1e8) + pbar = tqdm(total=args.train_total_steps) recent_100_reward = [] - total_step = 0 + total_steps = 0 max_reward = None - while True: + while total_steps < args.train_total_steps: # start epoch - total_reward, step = run_train_episode(env, agent, rpm) - total_step += step + total_reward, steps = run_train_episode(env, agent, rpm) + total_steps += steps pbar.set_description('[train]exploration:{}'.format(agent.exploration)) - pbar.update(step) + pbar.update(steps) - if total_step // args.test_every_steps == test_flag: + if total_steps // args.test_every_steps >= test_flag: + while total_steps // args.test_every_steps >= test_flag: + test_flag += 1 pbar.write("testing") eval_rewards = [] for _ in tqdm(range(3), desc='eval agent'): eval_reward = run_evaluate_episode(test_env, agent) eval_rewards.append(eval_reward) - test_flag += 1 logger.info( "eval_agent done, (steps, eval_reward): ({}, {})".format( - total_step, np.mean(eval_rewards))) - if total_step > 1e8: - break + total_steps, np.mean(eval_rewards))) pbar.close() @@ -141,10 +140,17 @@ if __name__ == '__main__': parser.add_argument('--rom', help='atari rom', required=True) parser.add_argument( '--batch_size', type=int, default=64, help='batch size for training') + parser.add_argument( + '--train_total_steps', + type=int, + default=int(1e8), + help='maximum training steps') parser.add_argument( '--test_every_steps', type=int, default=100000, - help='every steps number to run test') + help='the step interval between two consecutive evaluations') + args = parser.parse_args() + main() diff --git a/examples/PPO/README.md b/examples/PPO/README.md index c78a216..22c2690 100644 --- a/examples/PPO/README.md +++ b/examples/PPO/README.md @@ -11,8 +11,8 @@ Include following approach: Please see [here](https://github.com/openai/mujoco-py) to know more about Mujoco game. ### Benchmark result -- HalfCheetah-v2 - + +PPO_HalfCheetah-v2 ## How to use ### Dependencies: diff --git a/examples/PPO/train.py b/examples/PPO/train.py index 4b9ead5..7508f6f 100755 --- a/examples/PPO/train.py +++ b/examples/PPO/train.py @@ -120,11 +120,12 @@ def main(): # run a few episodes to initialize scaler collect_trajectories(env, agent, scaler, episodes=5) - episode = 0 - while episode < args.num_episodes: + test_flag = 0 + total_steps = 0 + while total_steps < args.train_total_steps: obs, actions, rewards = collect_trajectories( env, agent, scaler, episodes=args.episodes_per_batch) - episode += args.episodes_per_batch + total_steps += obs.shape[0] pred_values = agent.value_predict(obs) @@ -145,14 +146,16 @@ def main(): value_loss = agent.value_learn(obs, discount_sum_rewards) logger.info( - 'Episode {}, Train reward: {}, Policy loss: {}, KL: {}, Value loss: {}' - .format(episode, + 'Steps {}, Train reward: {}, Policy loss: {}, KL: {}, Value loss: {}' + .format(total_steps, np.sum(rewards) / args.episodes_per_batch, policy_loss, kl, value_loss)) - if episode % (args.episodes_per_batch * 5) == 0: + if total_steps // args.test_every_steps >= test_flag: + while total_steps // args.test_every_steps >= test_flag: + test_flag += 1 eval_reward = run_evaluate_episode(env, agent, scaler) - logger.info('Episode {}, Evaluate reward: {}'.format( - episode, eval_reward)) + logger.info('Steps {}, Evaluate reward: {}'.format( + total_steps, eval_reward)) if __name__ == "__main__": @@ -162,11 +165,6 @@ if __name__ == "__main__": type=str, help='Mujoco environment name', default='HalfCheetah-v2') - parser.add_argument( - '--num_episodes', - type=int, - help='Number of episodes to run', - default=10000) parser.add_argument( '--gamma', type=float, help='Discount factor', default=0.995) parser.add_argument( @@ -186,6 +184,16 @@ if __name__ == "__main__": type=str, help="Choose loss type of PPO algorithm, 'CLIP' or 'KLPEN'", default='CLIP') + parser.add_argument( + '--train_total_steps', + type=int, + default=int(1e7), + help='maximum training steps') + parser.add_argument( + '--test_every_steps', + type=int, + default=int(1e4), + help='the step interval between two consecutive evaluations') args = parser.parse_args() -- GitLab