Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration
The process of discovery requires active exploration -- the act of collecting new and informative data. However, efficient autonomous exploration remains a major unsolved problem. The dominant paradigm addresses this challenge by using Reinforcement Learning (RL) to train agents with intrinsic motivation, maximizing a composite objective of extrinsic and intrinsic rewards. We suggest that this approach incurs unnecessary overhead: while policy optimization is necessary for precise task execution, employing such machinery solely to expand state coverage may be inefficient. In this paper, we propose a new approach that explicitly decouples exploration from policy optimization and bypasses R...
| Authors | Zakaria Mhammedi, James Cohan |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 13 May 2026 |
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