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.

Slides presented at ICML 2016: https://www.dropbox.com/s/rqtpp1jv2jtzxeg/ICML2016_benchmarking_slides.pdf?dl=0