Helping RL Agents Survive Compounding Errors in Long-Horizon Planning
Mila_Quebec · x · 2026-07-07
Discusses how to prevent reinforcement learning agents from failing due to the accumulation of compounding errors during long-horizon planning. The proposed approach avoids reasoning directly over raw actions and instead adopts a hierarchical method for higher-level planning, thereby mitigating error amplification over long horizons.
Related event: Hierarchical Reasoning Proposed to Solve RL Long-Horizon Planning(2 posts)→
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