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.