Skip to content
Experiments of the paper "Hessian Aided Policy Gradient"
Python Jupyter Notebook JavaScript Shell HTML CSS Other
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.idea
Exp_paper
data/local
docker
docs
examples
garage
scripts
tests
vendor
.DS_Store
.editorconfig
.pre-commit-config.yaml
.travis.yml
CHANGELOG.md
CODEOWNERS
CONTRIBUTING.md
LICENSE
README.md
environment.yml
setup.cfg
setup.py

README.md

Hessian Aided Policy Gradient

This repo contains the code of the paper "Hessian Aided Policy Gradient". This code is based on the garage code repository thus can be installed in the same way. To run the experiments, examples are in Exp_paper/.

Docs Build Status License

garage

garage is a framework for developing and evaluating reinforcement learning algorithms. It includes a wide range of continuous control tasks plus implementations of algorithms.

garage is fully compatible with OpenAI Gym. All garage environments implement gym.Env, so all garage components can also be used with any environment implementing gym.Env.

garage only officially supports Python 3.5+.

garage comes with support for running reinforcement learning experiments on an EC2 cluster, and tools for visualizing the results. See the documentation for details.

garage supports TensorFlow and Theano for neural network frameworks. TensorFlow modules can be found under garage/tf.

Documentation

Documentation is available online at https://rlgarage.readthedocs.org/en/latest/.

Citing garage

If you use garage for academic research, you are highly encouraged to cite the following paper on the original rllab implementation:

Credits

garage is based on a predecessor project called rllab. The garage project is grateful for the contributions of the original rllab authors, and hopes to continue advancing the state of reproducibility in RL research in the same spirit.

rllab was originally developed by Rocky Duan (UC Berkeley/OpenAI), Peter Chen (UC Berkeley), Rein Houthooft (UC Berkeley/OpenAI), John Schulman (UC Berkeley/OpenAI), and Pieter Abbeel (UC Berkeley/OpenAI).

You can’t perform that action at this time.