virajmehta/hucrl

Implementation of H-UCRL Algorithm

This repository is an implementation of the H-UCRL algorithm introduced in Curi, S., Berkenkamp, F., & Krause, A. (2020). Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning.

To install create a conda environment:

$ conda create -n hucrl python=3.7
$ conda activate hucrl

$ pip install -e .[test,logging,experiments]

For Mujoco (license required) Run:

$ pip install -e .[mujoco]

Running an experiment.

For the inverted pendulum experiment run

$ python exps/inverted_pendulum/run.py

For the mujoco (license required) experiment run

$ python exps/mujoco/run.py --environment ENV_NAME --agent AGENT_NAME --action

We support MBHalfCheetah-v0, MBPusher-v0, MBReacher-v0, MBAnt-v0, MBCartPole-v0, MBHopper-v0, MBInvertedDoublePendulum-v0, MBInvertedPendulum-v0, MBReacher-v0, MBReacher3D-v0, MBSwimmer-v0, MBWalker2d-v0

Citing H-UCRL

If you this repo for your research please use the following BibTeX entry:

@article{curi2020efficient,
  title={Efficient model-based reinforcement learning through optimistic policy search and planning},
  author={Curi, Sebastian and Berkenkamp, Felix and Krause, Andreas},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}