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[ICLR 2023] Choreographer: a model-based agent that discovers and learns unsupervised skills in latent imagination, and it's able to efficiently coordinate and adapt the skills to solve downstream tasks.

Home Page: https://skillchoreographer.github.io/

License: MIT License

Python 95.46% Jupyter Notebook 4.54%
reinforcement-learning skill-learning world-models

choreographer's Introduction

The objective of my research is to build intelligent agents that discover and learn how to behave in the environment by interacting with it.

Repository Paper
choreographer Choreographer: Learning and Adapting Skills in Imagination [website] [arxiv]
mastering-urlb Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels [website] [arxiv]
redundancy-action-spaces Redundancy-aware Action Spaces for Robot Learning [website] [arxiv]
contrastive-aif Contrastive Active Inference [website] [arxiv]
lbs-exploration Curiosity-Driven Exploration via Latent Bayesian Surprise [website] [arxiv]

choreographer's People

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Forkers

vamorel cmeo97 ksb21st swy99 lookatator

choreographer's Issues

Choreographer in discrete action spaces

I am trying to run Choreographer in environments with discrete action spaces.

The implementation was done with the help of the part of DreamerV2 code that are responsible for discrete action space. But my implementation did not work very well, especially lbs shows somewhat collapsing of actions, which means that it selects only one action in pixel based environments.

This link shows a part of the changes I have made besides adding some env wrappers to envs.py: swy99@e27b256

I have mainly tried MiniGrid with different sets of hyperparameters but all my attempts failed.

Did you have any chances to try discrete action space environments? Could you share some working sets of hyperparameters?

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