资源算法 e-maml

e-maml

2020-02-25 | |  35 |   0 |   0

E-MAML Implementation

This repo contains the full implementation of the E-MAML algorithm from the paper Some Considerations on Learning to Explore via Meta-Reinforcement Learning

Structure of This Codebase

The main implementation is contained in the e_maml_tf directory. Inside the e_maml_experiments directory we provide a light weight half-cheetah baseline for verification. The original KrazyWorld codebase is not opensourced. So we implemented a new KrazyWorld environment. To run E-MAML on this new KrazyWorld, you need to add a thin adaptor following the convention in custom_vendor and sampler.py.

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