An LLM classifies game states into situations and selects the RL agent with the best historical average reward for each situation, outperforming static ensemble baselines on Atari.
A graph placement methodology for fast chip design.Nature, 594(7862):207–212, 2021
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Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One
An LLM classifies game states into situations and selects the RL agent with the best historical average reward for each situation, outperforming static ensemble baselines on Atari.