Plasticity Loss Impacts Reinforcement Learning in Noisy Environments

Researchers have investigated the impact of plasticity loss on reinforcement learning agents operating in stochastic environments. Their study, conducted on three DeepMind Control tasks with added noise, revealed that methods designed to preserve plasticity enhance learning robustness, particularly as observation noise increases. The findings also indicate that stochasticity can accelerate plasticity loss in more challenging environments, while easier settings may see noise acting as a regularizer. Plasticity loss was most evident in the early stages of training, with TD instability becoming a dominant failure mode in the most difficult stochastic settings. AI

IMPACT Understanding plasticity loss in RL agents could lead to more robust and reliable AI systems in complex, real-world scenarios.

RANK_REASON The cluster contains an academic paper detailing research findings on reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• DeepMind Control tasks
• layer normalization
• Plasticity Loss
• reinforcement learning
• TD instability

• paper
• other

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