Source code for paper: Efficient deep reinforcement learning via adaptive policy transfer
For reacher task, requirements follow:https://github.com/martinseilair/dm_control2gym
- [PTF code](#PTF code)
- Installation
- [Run an experiment](#Run an experiment)
- Configuration
- [Operating parameters](#Operating parameters)
- [Core parameters](#Core parameters)
- [Some experiences setting in paper](#Some experiences setting in paper)
- [In BibTeX format](#In BibTeX format)
- ptf
- alg (agent polices)
- A3C
- PPO
- DQN
- PTF_A3C
- PTF_PPO
- Caps
- config (Configuration parameters of each polices and game)
- a3c_conf
- ppo_conf
- ptf_a3c_conf
- ptf_ppo_conf
- particle_conf
- grid_conf
- pinball_conf
- reacher_conf
- game
- grid_game (grid make_env)
- pinball (pinball make_env)
- control2gym_game (control2gym make_env)
- run (execute the tasks)
- run_a3c
- run_ppo
- run_dqn
- run_ptf_a3c
- run_ptf_ppo
- run_caps
- source (opponent policies)
- util
- main (entry function)
- alg (agent polices)
python==3.6.5
pip install -r requirements.txt
python main.py -a ppo -c ppo_conf -g grid -d grid_conf task=324
some logs will be shown below:
win : False, step : 100, discounted_reward : 0.0, discount_reward_mean : 0.0, undiscounted_reward : 0, reward_mean : 0.0, episode : 1,
win : False, step : 100, discounted_reward : 0.0, discount_reward_mean : 0.0, undiscounted_reward : 0, reward_mean : 0.0, episode : 2,
All results will be stored in the results/alg_name/game_type/game_name/time folder, every folder contains graph, log, model, output, args.json, command.txt
If you do not want to save graph and model, setting param save_model=False.
graph: can usetensorboard --logdir=pathto check the tensorflow graph and loss in terminal.log: the print results in terminal.model: models saved everysave_per_episodesepisodes.output.json: reward results.args.json: store all params.command.txt: shell command.
Source policies contain pre-trained policies for source tasks. For example, in grid game, 4 pre-trained policies(goals are located at grid 81,459,65,295) are as source policies. Using test mode via -t load_model can reload the model to render
The config files act as defaults for an algorithm or environment.
They are all located in config.
python main.py -a ppo -c ppo_conf -g grid -d grid_conf task=324 Take the above example:
-a ppo: choose an algorithm.-c ppo_conf: choose corresponding algorithm configuration.-g grid: game type.-d grid_conf: game configuration.-t: evaluation the results, by setting-t True, and-t Falseas default.task=324: the goal is located at 324 position in grid game.option_model_path=[source_policies/grid/81/81,source_policies/grid/459/459,source_policies/grid/65/65,source_policies/grid/295/295]: load source policy for ptf_a3c and ptf_ppo.
Default:
option_layer_1=20option_batch_size=32c1=0.005e_greedy_increment=0.005learning_rate_o=0.00001, learning_rate_t=0.00001xi=0
# grid ptf_a3c
python main.py -a ptf_a3c -c ptf_a3c_conf -g grid -d grid_conf -n 20000 -e 99 -s 37 -o adam ENTROPY_BETA=0.0001 n_layer_a_1=100 n_layer_c_1=100 learning_rate_a=3e-4 learning_rate_c=3e-4 learning_rate_o=1e-3 learning_rate_t=1e-3 e_greedy=0.95 e_greedy_increment=1e-3 replace_target_iter=1000 option_batch_size=32 batch_size=32 reward_decay=0.99 option_model_path=[source_policies/grid/81/81,source_policies/grid/459/459,source_policies/grid/65/65,source_policies/grid/295/295] learning_step=10000 save_per_episodes=1000 save_model=True task=324 c1=0.001 N_WORKERS=8 USE_CPU_COUNT=False
# pinball ptf_a3c
python main.py -a ptf_a3c -c ptf_a3c_conf -g pinball -d pinball_conf -n 20000 -e 499 -s 1 -o adam ENTROPY_BETA=0.0001 n_layer_a_1=100 n_layer_c_1=100 learning_rate_a=3e-4 learning_rate_c=3e-4 learning_rate_o=3e-4 learning_rate_t=3e-4 e_greedy=0.95 e_greedy_increment=1e-3 replace_target_iter=1000 option_batch_size=32 batch_size=32 reward_decay=0.99 option_model_path=['source_policies/a3c/0.90.9/model','source_policies/a3c/0.90.2/model','source_policies/a3c/0.20.9/model'] learning_step=10000 save_per_episodes=1000 sequential_state=False continuous_action=True configuration=game/pinball_hard_single.cfg random_start=True start_position=[[0.6,0.4]] target_position=[0.1,0.1] c1=0.0005 source_policy=a3c save_model=True action_clip=1 reward_normalize=False
# reacher ptf_a3c
python main.py -a ptf_a3c -c ptf_a3c_conf -g reacher -d reacher_conf -n 10000 -e 1000 -s 17 -o adam ENTROPY_BETA=0.0003 n_layer_a_1=256 n_layer_c_1=256 learning_rate_a=1e-4 learning_rate_c=1e-4 learning_rate_o=3e-4 learning_rate_t=3e-4 e_greedy=0.95 e_greedy_increment=1e-2 replace_target_iter=5000 reward_decay=0.99 option_model_path=['source_policies/reacher/t1/model','source_policies/reacher/t2/model','source_policies/reacher/t3/model','source_policies/reacher/t4/model'] learning_step=10000 save_model=True task=hard c1=0.001 source_policy=a3c clip_value=10 batch_size=300 option_batch_size=16 reward_normalize=True done_reward=10 learning_times=1 option_layer_1=20 save_per_episodes=1000
# grid ptf_ppo
python main.py -a ptf_ppo -c ptf_ppo_conf -g grid -d grid_conf -n 20000 -e 99 -s 19 -o adam n_layer_a_1=100 n_layer_c_1=100 c2=0.005 learning_rate_a=3e-4 learning_rate_c=3e-4 learning_rate_o=3e-4 learning_rate_t=3e-4 reward_decay=0.99 clip_value=0.2 e_greedy=0.95 e_greedy_increment=1e-3 replace_target_iter=1000 option_batch_size=32 batch_size=32 option_model_path=[source_policies/grid/81/81,source_policies/grid/459/459,source_policies/grid/65/65,source_policies/grid/295/295] learning_step=10000 save_per_episodes=1000 save_model=True task=324 c3=0.0005
# pinball ptf_ppo
python main.py -a ptf_ppo -c ptf_ppo_conf -g pinball -d pinball_conf -n 20000 -e 499 -s 12345 -o adam n_layer_a_1=256 n_layer_c_1=256 c2=0.0001 learning_rate_a=3e-4 learning_rate_c=3e-4 learning_rate_o=1e-3 clip_value=0.2 learning_rate_t=1e-3 e_greedy=0.95 e_greedy_increment=5e-4 replace_target_iter=1000 option_batch_size=16 batch_size=32 reward_decay=0.99 option_model_path=['source_policies/a3c/0.90.9/model','source_policies/a3c/0.90.2/model','source_policies/a3c/0.20.9/model'] learning_step=10000 save_per_episodes=1000 sequential_state=False continuous_action=True configuration=game/pinball_hard_single.cfg start_position=[[0.6,0.4]] random_start=True target_position=[0.1,0.1] c1=0.01 source_policy=a3c save_model=True action_clip=1 reward_normalize=False option_layer_1=32
# Reacher ptf_ppo
python main.py -a ptf_ppo -c ptf_ppo_conf -g reacher -d reacher_conf -n 10000 -e 1000 -s 2 -o adam n_layer_a_1=256 n_layer_c_1=256 learning_rate_a=3e-4 learning_rate_c=3e-4 learning_rate_o=1e-3 learning_rate_t=1e-3 e_greedy=0.95 e_greedy_increment=1e-2 replace_target_iter=1000 reward_decay=0.99 option_model_path=['source_policies/reacher/t1/model','source_policies/reacher/t2/model','source_policies/reacher/t3/model','source_policies/reacher/t4/model'] learning_step=10000 save_per_episodes=1000 task=hard c1=0.001 source_policy=a3c clip_value=10 batch_size=300 option_batch_size=16 reward_normalize=True done_reward=10 option_layer_1=20
@inproceedings{Yang2020,
author = {Tianpei Yang and
Jianye Hao and
Zhaopeng Meng and
Zongzhang Zhang and
Yujing Hu and
Yingfeng Chen and
Changjie Fan and
Weixun Wang and
Wulong Liu and
Zhaodong Wang and
Jiajie Peng},
title = {Efficient Deep Reinforcement Learning via Adaptive Policy Transfer},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence},
pages = {3094--3100},
year = {2020}
}
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