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This paper introduces EFCA, a multi-timescale credit assignment method for agentic reinforcement learning that uses short-term feedback and medium-term state-history signals from environment interaction to improve task success and quality on ALFWorld and WebShop.
This paper introduces AutoPersonas, an architecture for long-term persona agents that prevents 'self-locking' by separating divergence from evidence-governed absorption, demonstrating reduced repetition and improved identity continuity in simulated environments.
This paper proposes a performance-driven state abstraction method for reinforcement learning that directly optimizes decision quality, using a multi-timescale framework to jointly adapt the policy and a tree-structured abstraction. The algorithm refines or aggregates state space based on Q-value discrepancies, achieving better sample efficiency and faster replanning than baselines.
This paper identifies surrogate hacking and temporal uncertainty as failure modes in multi-timescale RL, and proposes a Target Decoupling architecture that removes routing from the actor, using the critic for auxiliary representation learning. The method eliminates policy collapse on the LunarLander-v2 benchmark and stably surpasses the 'Environment Solved' threshold without hyperparameter hacking.