Meet Text2Reward: A Data-Free Framework that Automates the Generation of Dense Reward Functions

Meet Text2Reward: A Data-Free Framework that Automates the Generation of Dense Reward Functions Based on Large Language Models
Reward shaping, which seeks to develop reward functions that more effectively direct an agent towards desirable behaviors, is still a long-standing difficulty in reinforcement learning (RL). It is a time-consuming procedure that requires skill, might be sub-optimal, and is frequently done manually by constructing incentives based on expert intuition and heuristics. Reward shaping may be addressed via inverse reinforcement learning (IRL) and preference learning. A reward model can be taught using preference-based feedback or human examples. Both approaches still need significant labor or data collecting, and the neural network-based reward models need to be more comprehensible and unable to generalize outside the training data’s domains.
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Paper: arxiv.org/abs/...
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