# vime

**Repository Path**: mirrors_openai/vime

## Basic Information

- **Project Name**: vime
- **Description**: Code for the paper "Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks"
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-11
- **Last Updated**: 2026-02-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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 git@github.com: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`.