Successor-Predecessor Intrinsic Exploration Changmin Y u 1,2 Neil Burgess

Neural Information Processing Systems 

Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards.

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