rllab is a framework for developing and evaluating reinforcement learning algorithms. It includes a wide range of continuous control tasks plus implementations of the following algorithms:
- REINFORCE
- Truncated Natural Policy Gradient
- Reward-Weighted Regression
- Relative Entropy Policy Search
- Trust Region Policy Optimization
- Cross Entropy Method
- Covariance Matrix Adaption Evolution Strategy
- Deep Deterministic Policy Gradient
rllab is fully compatible with OpenAI Gym. See here for instructions and examples.
Documentation is available online: https://rllab.readthedocs.org/en/latest/.
If you use rllab for academic research, you are highly encouraged to cite the following paper:
- Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel. "Benchmarking Deep Reinforcement Learning for Continuous Control". Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016.