Maximum-Entropy Exploration with Future State-Action Visitation Measures
Maximum entropy reinforcement learning motivates agents to explore states and actions to maximize the entropy of some distribution, typically by providing additional intrinsic rewards proportional to that entropy function. In this paper, we study intrinsic rewards proportional to the entropy of the discounted distribution of state-action features visited during future time steps. This approach is motivated by two results. First, we show that the expected sum of these intrinsic rewards is a lower bound on the entropy of the discounted distribution of state-action features visited in trajectories starting from the initial states, which we relate to an alternative maximum entropy objective. ...
| Authors | Adrien Bolland, Gaspard Lambrechts, Damien Ernst |
|---|---|
| Primary category | cs.LG |
| Published | 19 March 2026 |
| Last updated at source | 13 October 2026 |
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