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About Learning To Walk Via Deep Reinforcement Learning
1) The document presents a new deep reinforcement learning algorithm based on maximum entropy reinforcement learning that is more sample efficient and requires less hyperparameter tuning than prior methods. 2) The algorithm is applied to learning walking gaits on a real-world quadruped robot (Minitaur) directly in the real world, requiring only about 400 rollouts (2 hours) to learn a stable gait without any prior modeling or simulation. 3) The learned policy can generalize to new terrains not seen during training and is robust to perturbations, demonstrating the potential of deep RL for automating locomotion control of legged robots.
Detailed Information
| Author: | ['Ziming Meng'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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