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
- Python 2.7
- gym
- tqdm
- OpenCV2 or Scipy
- TensorFlow 0.12.0
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).
Result of `Breakout-v0' for DQN without frame-skip (white-blue), DQN with frame-skip (light purple), Dueling DDQN (dark blue).
The hyperparameters and gradient clipping are not implemented as it is as [4].
References
Author
Taehoon Kim / @carpedm20
42 Sep 05, 2022
17 Nov 05, 2022
90 Dec 08, 2022
146 Nov 29, 2022
16 Nov 18, 2022
232 Nov 14, 2022
45 Dec 12, 2022
6 Nov 22, 2022
258 Dec 29, 2022
83 Jan 05, 2023
615 Dec 08, 2022
13 Dec 08, 2022
10 Sep 14, 2022
251 Jan 08, 2023
77 Dec 26, 2022
44 Nov 18, 2022
3 Feb 23, 2022
156 Nov 28, 2022
101 Nov 10, 2022
9 Sep 01, 2022