enjeeneer/zero-shot-rl
VC-FB and MC-FB algorithms from "Zero-Shot Reinforcement Learning from Low Quality Data" (NeurIPS 2024)
This project helps machine learning researchers improve how they train reinforcement learning agents when they only have a small or limited dataset to learn from. It takes existing, low-quality datasets and applies special algorithms to produce agents that can perform new tasks they haven't seen before, reducing overestimation errors. Researchers working on robotic control, automated decision-making, or simulation-based training would find this useful.
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Use this if you need to train intelligent agents to solve new problems without collecting massive, perfect datasets for every single task, especially for locomotion or goal-reaching robots.
Not ideal if your primary goal is to train a single agent for a single, well-defined task with an abundance of high-quality data.
Stars
26
Forks
2
Language
Python
License
MIT
Category
Last pushed
Jan 14, 2025
Commits (30d)
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