icaros-usc/dqd-rl
Official implementation of "Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning"
This project helps researchers create a wide variety of effective agent behaviors for complex environments. It takes simulated robot locomotion tasks as input and outputs diverse sets of policies, or strategies, that solve those tasks with different approaches. This is for researchers in artificial intelligence and robotics who are developing robust and versatile autonomous agents.
No commits in the last 6 months.
Use this if you need to generate a diverse collection of high-performing agent policies, rather than just one optimal policy, for simulated control tasks.
Not ideal if your primary goal is to find a single, globally optimal solution without concern for the diversity of behaviors.
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Language
Python
License
MIT
Category
Last pushed
Oct 03, 2022
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