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PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.
| Date | Stars |
|---|---|
| 2026-07-24 | 1285 |
| 2026-07-25 | 1285 |
| 2026-07-28 | 1285 |
| 2026-07-30 | 1285 |
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| 2026-08-06 | 1285 |
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# PyTorch implementation of reinforcement learning algorithms This repository contains: 1. policy gradient methods (TRPO, PPO, A2C) 2. [Generative Adversarial Imitation Learning (GAIL)](https://arxiv.org/pdf/1606.03476.pdf) ## Important notes - The code now works for PyTorch 0.4. For PyTorch 0.3, please check out the 0.3 branch. - To run mujoco environments, first install [mujoco-py](https://github.com/openai/mujoco-py) and [gym](https://github.com/openai/gym). - If you have a GPU, I recommend setting the OMP_NUM_THREADS to 1 (PyTorch will create additional threads when performing computations which can damage the performance of multiprocessing. This problem is most serious with Linux, where multiprocessing can be even slower than a single thread): ``` export OMP_NUM_THREADS=1 ``` ## Features * Support discrete and continous action space. * Support multiprocessing for agent to collect samples in multiple environments simultaneously. (x8 faster than single thread) * Fast Fisher vector product calculation. For this part, Ankur kindly wrote a [blog](http://www.telesens.co/2018/06/09/efficiently-computing-the-fisher-vector-product-in-trpo/) explaining the implementation details. ## Policy gradient methods * [Trust Region Policy Optimization (TRPO)](https://arxiv.org/pdf/1502.05477.pdf) -> [examples/trpo_gym.py](https://github.com/Khrylx/PyTorch-RL/blob/master/examples/trpo_gym.py) * [Proximal Policy Optimization (PPO)](https://arxiv.org/pdf/1707.06347.pdf) -> [examples/ppo_gym.py](https://github.com/Khrylx/PyTorch-RL/blob/master/examples/ppo_gym.py) * [Synchronous A3C (A2C)](https://arxiv.org/pdf/1602.01783.pdf) -> [examples/a2c_gym.py](https://github.com/Khrylx/PyTorch-RL/blob/master/examples/a2c_gym.py) ### Example * python examples/ppo_gym.py --env-name Hopper-v2 ### Reference * [ikostrikov/pytorch-trpo](https://github.com/ikostrikov/pytorch-trpo) * [openai/baselines](https://github.com/openai/baselines) ## Generative Adversarial Imitation Learning (GAIL) ### To save trajectory * python gail/save_expert_traj.py --model-path assets/learned_models/Hopper-v2_ppo.p ### To do imitation learning * python gail/gail_gym.py --env-name Hopper-v2 --expert-traj-path assets/expert_traj/Hopper-v2_expert_traj.p
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matched fp:6d80a8f12e909c1d, topic:reinforcement-learning, topic:deep-reinforcement-learning, desc:reinforcement learning