Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Yarats Denis
Yarats Denis
Zhang Amy
Zhang Amy
Amos Brandon
Amos Brandon
Cited by: 3|Bibtex|Views26
Other Links: arxiv.org

Abstract:

Training an agent to solve control tasks directly from high-dimensional images with model-free reinforcement learning (RL) has proven difficult. The agent needs to learn a latent representation together with a control policy to perform the task. Fitting a high-capacity encoder using a scarce reward signal is not only sample inefficient,...More

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