Improving Policy Gradient by Exploring Under-appreciated Rewards

28 Nov 2016 Ofir Nachum Mohammad Norouzi Dale Schuurmans

This paper presents a novel form of policy gradient for model-free reinforcement learning (RL) with improved exploration properties. Current policy-based methods use entropy regularization to encourage undirected exploration of the reward landscape, which is ineffective in high dimensional spaces with sparse rewards... (read more)

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Methods used in the Paper


METHOD TYPE
REINFORCE
Policy Gradient Methods
Entropy Regularization
Regularization