Plasticity Loss in Reinforcement Learning Studied in Stochastic Environments
Plasticity-preserving methods enhance robustness in noisy SAC tasks while stochasticity can regularize or accelerate plasticity loss.
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Stories tagged #deep-rl
Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints
A new operator stabilizes deep reinforcement learning when training runs under fully homomorphic encryption constraints.
A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning
Paper unifies regularization-based robust RL methods via new performance-gap upper bounds and jointly learned Lagrange multipliers.
Robust Policy Optimization via Adversarial Importance Sampling
Adversarial Importance Sampling estimates worst-case RL returns without extra interactions; authors also release the advrl PyTorch library.