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PyTorch implementation of deep reinforcement learning algorithms
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| 2026-07-31 | 486 |
| 2026-08-06 | 486 |
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# Deep Reinforcement Learning (DRL) Algorithms with PyTorch
This repository contains PyTorch implementations of deep reinforcement learning algorithms. **The repository will soon be updated including the PyBullet environments!**
## Algorithms Implemented
1. Deep Q-Network (DQN) <sub><sup> ([V. Mnih et al. 2015](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)) </sup></sub>
2. Double DQN (DDQN) <sub><sup> ([H. Van Hasselt et al. 2015](https://arxiv.org/abs/1509.06461)) </sup></sub>
3. Advantage Actor Critic (A2C)
4. Vanilla Policy Gradient (VPG)
5. Natural Policy Gradient (NPG) <sub><sup> ([S. Kakade et al. 2002](http://papers.nips.cc/paper/2073-a-natural-policy-gradient.pdf)) </sup></sub>
6. Trust Region Policy Optimization (TRPO) <sub><sup> ([J. Schulman et al. 2015](https://arxiv.org/abs/1502.05477)) </sup></sub>
7. Proximal Policy Optimization (PPO) <sub><sup> ([J. Schulman et al. 2017](https://arxiv.org/abs/1707.06347)) </sup></sub>
8. Deep Deterministic Policy Gradient (DDPG) <sub><sup> ([T. Lillicrap et al. 2015](https://arxiv.org/abs/1509.02971)) </sup></sub>
9. Twin Delayed DDPG (TD3) <sub><sup> ([S. Fujimoto et al. 2018](https://arxiv.org/abs/1802.09477)) </sup></sub>
10. Soft Actor-Critic (SAC) <sub><sup> ([T. Haarnoja et al. 2018](https://arxiv.org/abs/1801.01290)) </sup></sub>
11. SAC with automatic entropy adjustment (SAC-AEA) <sub><sup> ([T. Haarnoja et al. 2018](https://arxiv.org/abs/1812.05905)) </sup></sub>
## Environments Implemented
1. Classic control environments (CartPole-v1, Pendulum-v0, etc.) <sub><sup> (as described in [here](https://gym.openai.com/envs/#classic_control)) </sup></sub>
2. MuJoCo environments (Hopper-v2, HalfCheetah-v2, Ant-v2, Humanoid-v2, etc.) <sub><sup> (as described in [here](https://gym.openai.com/envs/#mujoco)) </sup></sub>
3. **PyBullet environments (HopperBulletEnv-v0, HalfCheetahBulletEnv-v0, AntBulletEnv-v0, HumanoidDeepMimicWalkBulletEnv-v1 etc.)** <sub><sup> (as described in [here](https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet/gym/pybullet_envs)) </sup></sub>
## Results (MuJoCo, PyBullet)
### MuJoCo environments
#### Hopper-v2
- Observation space: 8
- Action space: 3
#### HalfCheetah-v2
- Observation space: 17
- Action space: 6
#### Ant-v2
- Observation space: 111
- Action space: 8
#### Humanoid-v2
- Observation space: 376
- Action space: 17
### PyBullet environments
#### HopperBulletEnv-v0
- Observation space: 15
- Action space: 3
#### HalfCheetahBulletEnv-v0
- Observation space: 26
- Action space: 6
#### AntBulletEnv-v0
- Observation space: 28
- Action space: 8
#### HumanoidDeepMimicWalkBulletEnv-v1
- Observation space: 197
- Action space: 36
## Requirements
- [PyTorch](https://pytorch.org)
- [TensorBoard](https://pytorch.org/docs/stable/tensorboard.html)
- [gym](https://github.com/openai/gym)
- [mujoco-py](https://github.com/openai/mujoco-py)
- [PyBullet](https://pybullet.org/wordpress/)
## Usage
The repository's high-level structure is:
├── agents
└── common
├── results
├── data
└── graphs
└── save_model
### 1) To train the agents on the environments
To train all the different agents on PyBullet environments, follow these steps:
```commandline
git clone https://github.com/dongminlee94/deep_rl.git
cd deep_rl
python run_bullet.py
```
For other environments, change the last line to `run_cartpole.py`, `run_pendulum.py`, `run_mujoco.py`.
If you want to change configurations of the agents, follow this step:
```commandline
python run_bullet.py \
--env=HumanoidDeepMimicWalkBulletEnv-v1 \
--algo=sac-aea \
--phase=train \
--render=False \
--load=None \
--seed=0 \
--iterations=200 \
--steps_per_iter=5000 \
--max_step=1000 \
--tensorboard=True \
--gpu_index=0
```
### 2) To watch the learned agents on the above environments
To watch all the learned agents on PyBullet environments, follow these Excerpt of 4,483 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:78e8adb332523c5d, llm:Repository topics and description: 'deep-reinforcement-learning', 'a2c', 'ddpg', 'ddqn', 'dqn', 'ppo', 'sac', 'td3', 'trpo', 'pytorch' and description 'PyTorch implementation of deep reinforcement learning algorithms'.
matched fp:78e8adb332523c5d, llm:Repository topics and description: 'deep-reinforcement-learning', 'a2c', 'ddpg', 'ddqn', 'dqn', 'ppo', 'sac', 'td3', 'trpo', 'pytorch' and description 'PyTorch implementation of deep reinforcement learning algorithms'.
matched fp:78e8adb332523c5d, llm:Repository topics and description: 'deep-reinforcement-learning', 'a2c', 'ddpg', 'ddqn', 'dqn', 'ppo', 'sac', 'td3', 'trpo', 'pytorch' and description 'PyTorch implementation of deep reinforcement learning algorithms'.