xlang-ai/text2reward
[ICLR 2024 Spotlight] Text2Reward: Reward Shaping with Language Models for Reinforcement Learning
This tool helps AI researchers and reinforcement learning practitioners quickly define and refine reward functions for their agents. It takes natural language descriptions of desired agent behaviors and automatically generates executable reward code. This allows for faster experimentation and iteration on complex robotic tasks or simulated environments without extensive manual coding of reward signals.
201 stars. No commits in the last 6 months.
Use this if you are developing reinforcement learning agents and want to leverage language models to automatically generate and shape reward functions from text descriptions.
Not ideal if you are a beginner in reinforcement learning or not comfortable with command-line operations and basic Python environment setup.
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Last pushed
Dec 17, 2024
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