rail-berkeley/rlkit
PyTorch reinforcement learning framework with implementations of SAC, TD3, HER, DQN, and other RL algorithms.
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RLkit is a reinforcement learning framework and collection of RL algorithms implemented in PyTorch. It includes implementations of major RL algorithms such as Soft Actor Critic (SAC), Twin Delayed Deep Deterministic Policy Gradient (TD3), Hindsight Experience Replay (HER), Deep Q-Network (DQN), Advantage Weighted Actor Critic (AWAC), and others. The repository provides example scripts and documentation for training agents using these algorithms.
Frequently asked
- What is rail-berkeley/rlkit?
- PyTorch reinforcement learning framework with implementations of SAC, TD3, HER, DQN, and other RL algorithms.
- Is rlkit open source?
- Yes — rail-berkeley/rlkit is open source, released under the MIT license.
- What language is rlkit written in?
- rail-berkeley/rlkit is primarily written in Python.
- How popular is rlkit?
- rail-berkeley/rlkit has 2.9k stars on GitHub.
- Where can I find rlkit?
- rail-berkeley/rlkit is on GitHub at https://github.com/rail-berkeley/rlkit.