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Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning

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arxiv 2405.13861 v4 pith:FCA3Z4ZO submitted 2024-05-22 cs.LG

classification cs.LG
keywords learnlearningreinforcementtasksagentsdifferenceforwardhypothesis
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Traditionally, reinforcement learning (RL) agents learn to solve new tasks by updating their neural network parameters through interactions with the task environment. However, recent works demonstrate that some RL agents, after certain pretraining procedures, can learn to solve unseen new tasks without parameter updates, a phenomenon known as in-context reinforcement learning (ICRL). The empirical success of ICRL is widely attributed to the hypothesis that the forward pass of the pretrained agent neural network implements an RL algorithm. In this paper, we support this hypothesis by showing, both empirically and theoretically, that when a transformer is trained for policy evaluation tasks, it can discover and learn to implement temporal difference learning in its forward pass.

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  1. Meta-Prompt Optimization for LLM-Based Sequential Decision Making

    cs.LG 2025-02 conditional novelty 5.0 of 10

    EXPO uses adversarial bandit weighting over LLM-generated prompt variations to optimize the meta-prompt of LLM-based sequential decision-making agents, improving performance on optimization and bandit tasks.

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