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Found 3 papers, 2 papers with code
ADAPTER-RL: Adaptation of Any Agent using Reinforcement Learning
Deep Reinforcement Learning (DRL) agents frequently face challenges in adapting to tasks outside their training distribution, including issues with over-fitting, catastrophic forgetting and sample inefficiency.
Partial advantage estimator for proximal policy optimization
Estimation of value in policy gradient methods is a fundamental problem.
Joint action loss for proximal policy optimization
Instead, for each sub-action we calculate the loss separately, which is less prone to clipping during updates thereby making better use of samples.
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