arXiv:2106.13281
공식 코드 · Papers with Code
Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation
원문 초록
We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX. We present results on a suite of tasks inspired by the existing reinforcement learning literature, but remade in our engine. Additionally, we provide reimplementations of PPO, SAC, ES, and direct policy optimization in JAX that compile alongside our environments, allowing the learning algorithm and the environment processing to occur on the same device, and to scale seamlessly on accelerators. Finally, we include notebooks that facilitate training of performant policies on common OpenAI Gym MuJoCo-like tasks in minutes.
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Deep Reinforcement Learning with Gradient Eligibility Traces
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Exploring the robustness of TractOracle methods in RL-based tractography
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
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