https://github.com/rlbayes/rllabplusplus

https://github.com/rlbayes/rllabplusplus

Last synced: 8 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/rlbayes/rllabplusplus
• Owner: rlbayes
• License: other
• Created: 2017-01-08T13:29:45.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-07-21T00:29:30.000Z (almost 7 years ago)
• Last Synced: 2024-05-22T07:52:39.336Z (about 1 month ago)
• Language: Python
• Size: 1.14 MB
• Stars: 161
• Watchers: 10
• Forks: 42
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

Lists

• awesome-deeplearning-resources - A framework for developing and evaluating reinforcement learning algorithms

README

        
# rllab++

rllab++ is a framework for developing and evaluating reinforcement learning algorithms, built on [rllab](https://github.com/openai/rllab). It has the following implementations besides the ones implemented in rllab:

- [Q-Prop](https://arxiv.org/abs/1611.02247)

- [IPG](https://arxiv.org/abs/1706.00387)

- [DQN](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)

- [DDPG](https://arxiv.org/abs/1509.02971) 

- [NAF](https://arxiv.org/abs/1603.00748)

The codes are experimental, and may require tuning or modifications to reach the best reported performances.

# Installation

Please follow the basic installation instructions in [rllab documentation](https://rllab.readthedocs.io/en/latest/).

# Examples

From the [launchers](/sandbox/rocky/tf/launchers) directory, run the following, with optional additional flags defined in [launcher_utils.py](/sandbox/rocky/tf/launchers/launcher_utils.py):

```

python algo_gym_stub.py --exp= 

```

Flags include:

- algo\_name: trpo ([TRPO](https://arxiv.org/abs/1502.05477)), vpg (vanilla policy gradient), ddpg ([DDPG](https://arxiv.org/abs/1603.00748)), qprop ([Q-Prop](https://arxiv.org/abs/1611.02247) with trpo), etc. See [launcher_utils.py](/sandbox/rocky/tf/launchers/launcher_utils.py) for more variants.

- env\_name: [OpenAI Gym](https://gym.openai.com/) environment name, e.g. HalfCheetah-v1.

The experiment will be saved in /data/local/\. 

# Citations

If you use rllab++ for academic research, you are highly encouraged to cite the following papers:

- Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schoelkopf, Sergey Levine. "[Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning](https://arxiv.org/abs/1706.00387)". arXiv:1706.00387 [cs.LG], 2017.

- Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine. "[Q-Prop: Sample-Efficient Policy Gradient with an Off-Policy Critic](https://arxiv.org/abs/1611.02247)" Proceedings of the International Conference on Learning Representations (ICLR), 2017. 

- Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel. "[Benchmarking Deep Reinforcement Learning for Continuous Control](http://arxiv.org/abs/1604.06778)". _Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016._