Learning and deploying robust locomotion policies with minimal dynamics randomization
Training Deep Reinforcement Learning (DRL) locomotion policies often require massive amounts of data to converge to the desired behavior. In this regard, simulators provide a cheap and abundant source. For successful sim-to-real transfer, xhaustively engineered approaches such as system identificati...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Proceedings of Machine Learning Research
2024
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