Skip to content

TensorFlow implementation of Deep Reinforcement Learning papers

License

Notifications You must be signed in to change notification settings

dillonalaird/deep-rl-tensorflow

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

55 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Deep Reinforcement Learning in TensorFlow

TensorFlow implementation of Deep Reinforcement Learning papers. This implementation contains:

[1] Playing Atari with Deep Reinforcement Learning
[2] Human-Level Control through Deep Reinforcement Learning
[3] Deep Reinforcement Learning with Double Q-learning
[4] Dueling Network Architectures for Deep Reinforcement Learning
[5] Prioritized Experience Replay (in progress)
[6] Deep Exploration via Bootstrapped DQN (in progress)
[7] Asynchronous Methods for Deep Reinforcement Learning (in progress)
[8] Continuous Deep q-Learning with Model-based Acceleration (in progress)

Requirements

Usage

First, install prerequisites with:

$ pip install -U 'gym[all]' tqdm scipy

Don't forget to also install the latest TensorFlow. Also note that you need to install the dependences of doom-py which is required by gym[all]

Train with DQN model described in [1] without gpu:

$ python main.py --network_header_type=nips --env_name=Breakout-v0 --use_gpu=False

Train with DQN model described in [2]:

$ python main.py --network_header_type=nature --env_name=Breakout-v0

Train with Double DQN model described in [3]:

$ python main.py --double_q=True --env_name=Breakout-v0

Train with Deuling network with Double Q-learning described in [4]:

$ python main.py --double_q=True --network_output_type=dueling --env_name=Breakout-v0

Train with MLP model described in [4] with corridor environment (useful for debugging):

$ python main.py --network_header_type=mlp --network_output_type=normal --observation_dims='[16]' --env_name=CorridorSmall-v5 --t_learn_start=0.1 --learning_rate_decay_step=0.1 --history_length=1 --n_action_repeat=1 --t_ep_end=10 --display=True --learning_rate=0.025 --learning_rate_minimum=0.0025
$ python main.py --network_header_type=mlp --network_output_type=normal --double_q=True --observation_dims='[16]' --env_name=CorridorSmall-v5 --t_learn_start=0.1 --learning_rate_decay_step=0.1 --history_length=1 --n_action_repeat=1 --t_ep_end=10 --display=True --learning_rate=0.025 --learning_rate_minimum=0.0025
$ python main.py --network_header_type=mlp --network_output_type=dueling --observation_dims='[16]' --env_name=CorridorSmall-v5 --t_learn_start=0.1 --learning_rate_decay_step=0.1 --history_length=1 --n_action_repeat=1 --t_ep_end=10 --display=True --learning_rate=0.025 --learning_rate_minimum=0.0025
$ python main.py --network_header_type=mlp --network_output_type=dueling --double_q=True --observation_dims='[16]' --env_name=CorridorSmall-v5 --t_learn_start=0.1 --learning_rate_decay_step=0.1 --history_length=1 --n_action_repeat=1 --t_ep_end=10 --display=True --learning_rate=0.025 --learning_rate_minimum=0.0025

Results

Result of Corridor-v5 in [4] for DQN (purple), DDQN (red), Dueling DQN (green), Dueling DDQN (blue).

model

Result of `Breakout-v0' for DQN without frame-skip (white-blue), DQN with frame-skip (light purple), Dueling DDQN (dark blue).

model

The hyperparameters and gradient clipping are not implemented as it is as [4].

References

Author

Taehoon Kim / @carpedm20

About

TensorFlow implementation of Deep Reinforcement Learning papers

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 98.0%
  • Shell 2.0%