rll/rllab
A Python framework for developing and evaluating reinforcement learning algorithms, compatible with OpenAI Gym.
Not currently ranked — collecting fresh signals.
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rllab provides a structured environment for building and testing reinforcement learning algorithms. It includes implementations of classic RL algorithms such as REINFORCE and Truncated Natural Policy Gradient, supports TensorFlow, and integrates with TensorBoard for monitoring training progress. The framework is fully compatible with the OpenAI Gym environment interface, allowing researchers to benchmark algorithms on standardized continuous control tasks.
Frequently asked
- What is rll/rllab?
- A Python framework for developing and evaluating reinforcement learning algorithms, compatible with OpenAI Gym.
- Is rllab open source?
- Yes — rll/rllab is an open-source project tracked on heatdrop.
- What language is rllab written in?
- rll/rllab is primarily written in Python.
- How popular is rllab?
- rll/rllab has 3.1k stars on GitHub.
- Where can I find rllab?
- rll/rllab is on GitHub at https://github.com/rll/rllab.