2,735 research outputs found
VIME: Variational Information Maximizing Exploration
Scalable and effective exploration remains a key challenge in reinforcement
learning (RL). While there are methods with optimality guarantees in the
setting of discrete state and action spaces, these methods cannot be applied in
high-dimensional deep RL scenarios. As such, most contemporary RL relies on
simple heuristics such as epsilon-greedy exploration or adding Gaussian noise
to the controls. This paper introduces Variational Information Maximizing
Exploration (VIME), an exploration strategy based on maximization of
information gain about the agent's belief of environment dynamics. We propose a
practical implementation, using variational inference in Bayesian neural
networks which efficiently handles continuous state and action spaces. VIME
modifies the MDP reward function, and can be applied with several different
underlying RL algorithms. We demonstrate that VIME achieves significantly
better performance compared to heuristic exploration methods across a variety
of continuous control tasks and algorithms, including tasks with very sparse
rewards.Comment: Published in Advances in Neural Information Processing Systems 29
(NIPS), pages 1109-111
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