/deep_rl

PyTorch implementations of Deep Reinforcement Learning algorithms (DQN, DDQN, A2C, VPG, TRPO, PPO, DDPG, TD3, SAC, ASAC, TAC, ATAC)

Primary LanguagePython

Deep Reinforcement Learning (DRL) Algorithms with PyTorch

This repository contains PyTorch implementations of deep reinforcement learning algorithms. This implementation uses PyTorch. For a TensorFlow implementation of algorithms, take a look at tsallis_actor_critic_mujoco.

Algorithms Implemented

  1. Deep Q-Network (DQN) (V. Mnih et al. 2015)
  2. Double DQN (DDQN) (H. Van Hasselt et al. 2015)
  3. Advantage Actor Critic (A2C)
  4. Vanilla Policy Gradient (VPG)
  5. Natural Policy Gradient (NPG) (S. Kakade et al. 2002)
  6. Trust Region Policy Optimization (TRPO) (J. Schulman et al. 2015)
  7. Proximal Policy Optimization (PPO) (J. Schulman et al. 2017)
  8. Deep Deterministic Policy Gradient (DDPG) (T. Lillicrap et al. 2015)
  9. Twin Delayed DDPG (TD3) (S. Fujimoto et al. 2018)
  10. Soft Actor-Critic (SAC) (T. Haarnoja et al. 2018)
  11. Automating entropy adjustment on SAC (ASAC) (T. Haarnoja et al. 2018)
  12. Tsallis Actor-Critic (TAC) (K. Lee et al. 2019)
  13. Automating entropy adjustment on TAC (ATAC)

Environments Implemented

  1. CartPole-v1 (as described in here)
  2. Pendulum-v0 (as described in here)
  3. MuJoCo environments (HalfCheetah-v2, Ant-v2, Humanoid-v2, etc.) (as described in here)

Results

CartPole-v1

  • Observation space: 4
  • Action space: 2

Pendulum-v0

  • Observation space: 3
  • Action space: 1

HalfCheetah-v2

  • Observation space: 17
  • Action space: 6

Ant-v2

  • Observation space: 111
  • Action space: 8

Humanoid-v2

  • Observation space: 376
  • Action space: 17

Requirements

Usage

The repository's high-level structure is:

├── agents                    
    └── common 
├── results  
    ├── data 
    └── graphs        
├── tests
    └── save_model

1) To train the agents on the environments

To train all the different agents on MuJoCo environments, follow these steps:

git clone https://github.com/dongminlee94/deep_rl.git
cd deep_rl
python run_mujoco.py

For other environments, change the last line to run_cartpole.py, run_pendulum.py.

If you want to change configurations of the agents, follow this step:

python run_mujoco.py \
    --env=Humanoid-v2 \
    --algo=atac \
    --seed=0 \
    --iterations=200 \
    --steps_per_iter=5000 \
    --max_step=1000

2) To watch the learned agents on the above environments

To watch all the learned agents on MuJoCo environments, follow these steps:

cd tests
python mujoco_test.py --load=envname_algoname_...

You should copy the saved model name in tests/save_model/envname_algoname_... and paste the copied name in envname_algoname_.... So the saved model will be load.