TalkRL: The Reinforcement Learning Podcast
Pierluca D'Oro and Martin Klissarov
Nov 13, 2023
Pierluca D'Oro and Martin Klissarov discuss their recent work on 'Motif, Intrinsic Motivation from AI Feedback' and its application in NetHack. They also explore the similarities between RL and Learning from Preferences, the challenges of training an RL agent for NetHack, the gap between RL and language models, and the difference between return and loss landscapes in RL.
57:24
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Quick takeaways
- Motif is a reinforcement learning approach that uses intrinsic reward to guide agent behavior and credit assignment based on language model knowledge.
- The authors explore the return landscape to gain insights into the behavior of reinforcement learning algorithms and improve the stability of RL agents.
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Overview of motif
Motif is a reinforcement learning approach that leverages the knowledge from language models to distill decision-making knowledge without direct interaction in the environment. It uses intrinsic reward, a reward specific to the agent, to drive exploration and credit assignment. By using captions generated by the language model to understand the underlying situation, motif creates a step-by-step reward in collaboration with RLHF. The work focuses on the game NetHack, where the language model has prior knowledge, and explores how to use this intrinsic reward to guide the agent's behavior.
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