Successor-Predecessor Intrinsic Exploration
This addresses exploration challenges in reinforcement learning for environments with sparse rewards, offering a novel approach that improves efficiency and performance, though it is incremental as it builds on existing intrinsic reward methods.
The paper tackles the problem of exploration in sparse-reward reinforcement learning by proposing SPIE, an algorithm that combines prospective and retrospective information in intrinsic rewards, resulting in more efficient exploration and stronger performance on sparse-reward Atari games than existing methods.
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. Although the study of intrinsic rewards has a long history, existing methods focus on composing the intrinsic reward based on measures of future prospects of states, ignoring the information contained in the retrospective structure of transition sequences. Here we argue that the agent can utilise retrospective information to generate explorative behaviour with structure-awareness, facilitating efficient exploration based on global instead of local information. We propose Successor-Predecessor Intrinsic Exploration (SPIE), an exploration algorithm based on a novel intrinsic reward combining prospective and retrospective information. We show that SPIE yields more efficient and ethologically plausible exploratory behaviour in environments with sparse rewards and bottleneck states than competing methods. We also implement SPIE in deep reinforcement learning agents, and show that the resulting agent achieves stronger empirical performance than existing methods on sparse-reward Atari games.