https://github.com/siddk/rl-kitchen-sink
PyTorch Implementations of Standard Deep RL Algorithms (including REINFORCE, A2C, PPO)
https://github.com/siddk/rl-kitchen-sink
a2c ppo pytorch pytorch-rl reinforce reinforcement-learning reinforcement-learning-algorithms
Last synced: 3 months ago
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PyTorch Implementations of Standard Deep RL Algorithms (including REINFORCE, A2C, PPO)
- Host: GitHub
- URL: https://github.com/siddk/rl-kitchen-sink
- Owner: siddk
- Created: 2018-07-26T19:26:57.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-09-11T19:11:03.000Z (almost 7 years ago)
- Last Synced: 2025-02-10T01:44:16.485Z (5 months ago)
- Topics: a2c, ppo, pytorch, pytorch-rl, reinforce, reinforcement-learning, reinforcement-learning-algorithms
- Language: Python
- Size: 17.6 KB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Reinforcement Learning Kitchen Sink
PyTorch Implementations of Standard Deep RL Algorithms (including REINFORCE, A2C, PPO). Each of the below algorithms
are implemented for both OpenAI Gym Classic Control Tasks (e.g. Cartpole, MountainCar), as well as the Atari suite.
This repository exists mostly as a means to illustrate basic RL algorithms via extremely readable and well-documented
PyTorch implementations. This repository also stores hyperparameters and learning curves (with confidence intervals!)
for each task.
This code is inspired in equal parts from the [OpenAI Baselines](https://github.com/openai/baselines) and Ilya
Kostrikov's [PyTorch-RL Repository](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr).
### REINFORCE
### Advantage Actor-Critic (A2C)
### Proximal Policy Optimization