Researchers at Oxford Presented Policy-Guided Diffusion: A Machine Learning Method for Controllable Generation of Synthetic Trajectories in Offline Reinforcement Learning RL

Reinforcement learning (RL) faces challenges due to sample inefficiency, hindering real-world adoption. Standard RL methods struggle, particularly in environments where exploration is risky. However, offline RL utilizes pre-collected data to optimize policies without online data collection. Yet, a distribution shift between the target policy and collected data presents hurdles, leading to an out-of-sample issue. This […]

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