https://github.com/openai/vime
Code for the paper "Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks"
https://github.com/openai/vime
paper
Last synced: 2 months ago
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Code for the paper "Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks"
- Host: GitHub
- URL: https://github.com/openai/vime
- Owner: openai
- Created: 2016-06-07T17:53:42.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2018-11-22T00:40:29.000Z (over 6 years ago)
- Last Synced: 2025-03-31T11:08:09.067Z (2 months ago)
- Topics: paper
- Language: Python
- Homepage: https://arxiv.org/abs/1605.09674
- Size: 23.4 KB
- Stars: 342
- Watchers: 182
- Forks: 92
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
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README
**Status:** Archive (code is provided as-is, no updates expected)
# How to run VIME
Variational Information Maximizing Exploration (VIME) as presented in Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks by *R. Houthooft, X. Chen, Y. Duan, J. Schulman, F. De Turck, P. Abbeel* (http://arxiv.org/abs/1605.09674).
To reproduce the results, you should first have [rllab](https://github.com/rllab/rllab) and Mujoco v1.31 configured. Then, run the following commands in the root folder of `rllab`:
```bash
git submodule add -f [email protected]:openai/vime.git sandbox/vime
touch sandbox/__init__.py
```
Then you can do the following:
- Execute TRPO+VIME on the hierarchical SwimmerGather environment via `python sandbox/vime/experiments/run_trpo_expl.py`.