How ICPL Addresses the Core Problem of RL Reward Design | HackerNoon
Briefly

from Hackernoon3 months ago
Our method, In-Context Preference Learning (ICPL), creatively integrates large language models with human preferences to synthesize effective reward functions within varying environments.
Hackernoonhttps://hackernoon.com/how-icpl-addresses-the-core-problem-of-rl-reward-design
ICPL utilizes contextual task information to allow the LLM to synthesize executable reward functions, refining them through iterative training of agents and video ranking.
Hackernoonhttps://hackernoon.com/how-icpl-addresses-the-core-problem-of-rl-reward-design
By generating videos of agent behaviors, ICPL forms a ranking system that helps identify positive and negative rewards, making human preference feedback more efficient.
Hackernoonhttps://hackernoon.com/how-icpl-addresses-the-core-problem-of-rl-reward-design
Read at Hackernoon
#in-context-preference-learning #reward-functions #llms #human-preferences #machine-learning
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