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ARXIV:2605.14982 · REINFORCEMENT LEARNING · SUBMITTED 15 MAY · 20:15 UTC · FRESHNESS FRESH
ARXIV:2605.14982REINFORCEMENT LEARNINGSUBMITTED 15 MAY · 20:15 UTCFRESHNESS FRESHSanjeev Manivannan · Shuban V · arXiv
Introduces second-order actor-critic methods for discounted Markov Decision Processes by decomposing policy Hessians for accelerated convergence.
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Pain Introduces second-order actor-critic methods for discounted Markov Decision Processes by decomposing policy Hessians for accelerated convergence.
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Introduces second-order actor-critic methods for discounted Markov Decision Processes by decomposing policy Hessians for accelerated convergence. To mitigate the value approximation challenges in policy gradient methods, actor-critic approaches have been developed and are known…
We address the discounted reward setting in reinforcement learning (RL). To mitigate the value approximation challenges in policy gradient methods, actor-critic approaches have been developed and are known to converge to stationary points under…
ScienceToStartup currently rates this 1.0/10 on the public viability pass. We show that this approximation becomes well-justified under a two-timescale actor-critic framework, where the critic evolves on a faster timescale and can be treated…
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 1.0/10.
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Introduces second-order actor-critic methods for discounted Markov Decision Processes by decomposing policy Hessians for accelerated convergence.
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10.48550/arXiv.2605.14982Introduces second-order actor-critic methods for discounted Markov Decision Processes by decomposing policy Hessians for accelerated convergence.
Abstract
We address the discounted reward setting in reinforcement learning (RL). To mitigate the value approximation challenges in policy gradient methods, actor-critic approaches have been developed and are known to converge to stationary points under suitable assumptions. However, these methods rely on first-order updates. In contrast, second-order optimization provides principled curvature-aware updates that are proven to accelerate convergence, but its application in RL is limited by the computational complexity of Hessian estimation. In this work, we analyze second-order approximations for the actor update that leverage the full curvature information of the objective as much as possible. A stable approximation requires treating the action-value function as locally constant with respect to policy parameters, which does not generally hold in policy gradient methods. We show that this approximation becomes well-justified under a two-timescale actor-critic framework, where the critic evolves on a faster timescale and can be treated as quasi-stationary during actor updates. Building on this insight, we formulate a second-order actor-critic method for the discounted reward setting that leverages Hessian-vector product (HVP) computations, resulting in a computationally efficient and stable second-order update.
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Introduces second-order actor-critic methods for discounted Markov Decision Processes by decomposing policy Hessians for accelerated convergence. To mitigate the value approximation challenges in policy gradient methods, actor-critic approaches have been developed and are known...
METHOD
We address the discounted reward setting in reinforcement learning (RL). To mitigate the value approximation challenges in policy gradient methods, actor-critic approaches have been developed and are known to converge to stationary points under suitable assumptions.
RESULT
ScienceToStartup currently rates this 1.0/10 on the public viability pass. We show that this approximation becomes well-justified under a two-timescale actor-critic framework, where the critic evolves on a faster timescale and can be treated as quasi-stationary during actor upda...
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Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 1.0/10.
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Introduces second-order actor-critic methods for discounted Markov Decision Processes by decomposing policy Hessians for accelerated convergence. To mitigate the value approximation challenges in policy gradient methods, actor-critic approaches have been developed and are known to converge to stationary points under suitable assumptions.
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partial
We address the discounted reward setting in reinforcement learning (RL). To mitigate the value approximation challenges in policy gradient methods, actor-critic approaches have been developed and are known to converge to stationary points under suitable assumptions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 1.0/10 on the public viability pass. We show that this approximation becomes well-justified under a two-timescale actor-critic framework, where the critic evolves on a faster timescale and can be treated as quasi-stationary during actor updates.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified May 2026. Public score 1.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Introduces second-order actor-critic methods for discounted Markov Decision Processes by decomposing policy Hessians for accelerated convergence.
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