Summary of Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation, by Eliot Xing et al.
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation
by Eliot Xing, Vernon Luk, Jean Oh
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent advancements in GPU-based parallel simulation have enabled the collection of large amounts of data and training of complex control policies using deep reinforcement learning (RL) on commodity GPUs. However, these successes for RL in robotics have been limited to tasks sufficiently simulated by fast rigid-body dynamics. To address this challenge, a novel RL algorithm and simulation platform are presented to enable scaling RL on tasks involving rigid bodies and deformables. The paper introduces Soft Analytic Policy Optimization (SAPO), a maximum entropy first-order model-based actor-critic RL algorithm that uses first-order analytic gradients from differentiable simulation to train a stochastic actor to maximize expected return and entropy. Alongside this approach, the parallel differentiable multiphysics simulation platform Rewarped is developed, which supports simulating various materials beyond rigid bodies. The paper demonstrates SAPO outperforming baselines over a range of tasks that involve interaction between rigid bodies, articulations, and deformables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to train robots using deep reinforcement learning (RL) when the task involves soft bodies like flexible materials or deformable objects. This is important because current RL methods are limited to tasks that can be simulated quickly with rigid-body dynamics. The authors introduce a new algorithm called SAPO and a simulation platform called Rewarped, which allows for more complex simulations. They show that SAPO works better than other approaches on tasks that involve interacting with both rigid bodies and soft materials. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
