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This work considers agent memory as a temporally-extended abstraction over the agent’s observation-action history, and derives POMDP classes by applying traditional MDP state abstractions, such as model-preservation, optimal value Q ∗ preservation, and optimal policy π ∗ preservation, to the POMDP setting.
This work introduces tentative object pruning, an approach that leverages this property by constructing multiple simplified tasks with reduced object sets and searching them in parallel until a valid, satisficing solution is found.
EvalXRL is a benchmark in which a Large Language Model (LLM) coding agent uses different XRL methods to diagnose a held-out malfunction in an RL agent, and then repair it, and proposes the first head-to-head comparison of multiple XRL methods in closed-loop usage.
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