HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
Advances in neural information processing systems , volume=
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Introduces the Agent State-Markov Policy Gradient (ASMPG) algorithm and a policy gradient theorem for non-Markovian decision processes by jointly optimizing agent state dynamics and control policy.
Language models engage in counterexample-repair loops for conceptual definitions but produce increasingly verbose outputs without accuracy gains and hit diminishing returns quickly.
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.
The note claims linear convergence of WPO in entropy-regularized MDPs by combining mean-field gradient flow analysis with a local log-Sobolev inequality under a regularity assumption.
citing papers explorer
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Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
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Policy Gradient Methods for Non-Markovian Reinforcement Learning
Introduces the Agent State-Markov Policy Gradient (ASMPG) algorithm and a policy gradient theorem for non-Markovian decision processes by jointly optimizing agent state dynamics and control policy.
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The Counterexample Game: Iterated Conceptual Analysis and Repair in Language Models
Language models engage in counterexample-repair loops for conceptual definitions but produce increasingly verbose outputs without accuracy gains and hit diminishing returns quickly.
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Using Common Random Numbers for Simulation-based Planning with Rollouts
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.
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A note on convergence of Wasserstein policy optimization
The note claims linear convergence of WPO in entropy-regularized MDPs by combining mean-field gradient flow analysis with a local log-Sobolev inequality under a regularity assumption.