most citedReverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement Learning

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cs.LG2025

Staggered Environment Resets Improve Massively Parallel On-Policy Reinforcement Learning

Sid Bharthulwar, Stone Tao, Hao Su

Massively parallel GPU simulation environments have accelerated reinforcement learning (RL) research by enabling fast data collection for on-policy RL algorithms like Proximal Poli…

cs.LG2024★ 1 cited

Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement Learning

Stone Tao, Arth Shukla, Tse-kai Chan +1

Reinforcement learning (RL) presents a promising framework to learn policies through environment interaction, but often requires an infeasible amount of interaction data to solve c…