jayLEE0301/dhrl_official
Official code for "DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning" (NeurIPS 2022 Oral)
This project offers a method for training AI agents to perform complex, multi-step tasks more efficiently. It takes in environment parameters and desired long-term goals, and outputs a trained agent capable of solving challenging problems like intricate robot locomotion or manipulation. AI researchers and robotics engineers working on autonomous systems or simulated agents would find this useful.
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Use this if you are developing AI agents for long-horizon tasks where rewards are sparse, and you need faster training and improved performance compared to standard hierarchical reinforcement learning methods.
Not ideal if you are working with simple, short-term tasks or if your reinforcement learning environment does not require a hierarchical approach.
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34
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5
Language
Python
License
MIT
Category
Last pushed
Jan 23, 2023
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