BRRL derives an analytic optimal policy for regularized constrained RL that guarantees monotonic improvement and yields the BPO algorithm that matches or exceeds PPO.
Neural trust region/proximal policy optimization attains globally optimal policy.Advances in neural information processing systems, 32
2 Pith papers cite this work. Polarity classification is still indexing.
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Differential privacy in policy optimization adds sample complexity costs that often appear as lower-order terms rather than dominating the bounds.
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Bounded Ratio Reinforcement Learning
BRRL derives an analytic optimal policy for regularized constrained RL that guarantees monotonic improvement and yields the BPO algorithm that matches or exceeds PPO.
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On the Sample Complexity of Differentially Private Policy Optimization
Differential privacy in policy optimization adds sample complexity costs that often appear as lower-order terms rather than dominating the bounds.