QOED selects identifiable parameter directions via Fisher matrix eigenspace analysis and modifies exploration objectives to approximate ideal information gain under bounded nuisance assumptions, yielding 21-35% performance gains in robotic tasks.
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SSE improves long-horizon goal-conditioned RL by using failure and partial-success transitions to identify unreliable subgoals, streamline high-level planning, and outperform prior hierarchical methods on benchmarks.
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Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
QOED selects identifiable parameter directions via Fisher matrix eigenspace analysis and modifies exploration objectives to approximate ideal information gain under bounded nuisance assumptions, yielding 21-35% performance gains in robotic tasks.
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Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning
SSE improves long-horizon goal-conditioned RL by using failure and partial-success transitions to identify unreliable subgoals, streamline high-level planning, and outperform prior hierarchical methods on benchmarks.