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Accelerated Policy Learning with Parallel Differentiable Simulation
[article]
2022
arXiv
pre-print
Deep reinforcement learning can generate complex control policies, but requires large amounts of training data to work effectively. Recent work has attempted to address this issue by leveraging differentiable simulators. However, inherent problems such as local minima and exploding/vanishing numerical gradients prevent these methods from being generally applied to control tasks with complex contact-rich dynamics, such as humanoid locomotion in classical RL benchmarks. In this work we present a
arXiv:2204.07137v1
fatcat:7mc3mbb4cnglrh3auxavtvb47m