This paper explores the potential-theoretic viewpoint for core reinforcement learning representations and algorithms under a fixed-policy assumption and may offer a path for improved sample efficiency and formal constraints that can be applied to RL.
Abstract
Reinforcement learning (RL) theory fundamentally depends on probability theory through the Markov chain. There is a deep connection between probability theory and potential theory. This paper reviews that connection and explores the potential-theoretic viewpoint for core reinforcement learning representations and algorithms under a fixed-policy assumption. This viewpoint may offer a path for improved sample efficiency and formal constraints that can be applied to RL. When the fixed-policy assumption is relaxed, the linear potential theory framework can be naturally extended to the nonlinear case.
This thesis develops diffusion-based world models, investigates RL for efficient video generation, explores generative models as policy classes, and studies interactive video world models in which actions shape future observations, and addresses long-horizon modeling through architectures with memory.
Reinforcement learning (RL) is increasingly grounded in tools from probability, optimization, and operator theory. This survey organizes the mathematical structures that underpin the design and analysis of modern algorithms in RL. We begin from Markov decision processes (MDPs) and Bellman operators, emphasizing contrac...
D. Belomestny, Alexander V. Gasnikov, E. Gladin et al.· Успехи математических наук· 0 citations
Reward design is a critical yet often underexplored
component of reinforcement learning for planning problems.
In this work we analyze how different reward shaping
strategies affect learning performance when using Proximal
Policy Optimization (PPO). We evaluate several reward
formulations derived from the objective fun...
Ol-i. Shuhailo, Mohsen Ghaffari, Karel Chvalovský et al.· Proceedings of the Internati...· 0 citations
The paper concludes that convergence guarantees degrade in a predictable, representation-dependent order as function approximation grows more expressive, and identifies extending stochastic approximation theory to nonlinear function approximation as the central future research prospect.
Shirish Prabhakarrao Kulkarni, C. Ashwini, M. Buvanasankari et al.· International journal of com...· 0 citations
This paper explores the efficacy of quasi-Monte Carlo (QMC) weight initialization for meta-reinforcement learning within modern benchmark environments and finds the orthogonal orientation was globally superior for an unbiased search.
This tutorial introduces adaptive control, actor-critic reinforcement algorithms, and a new way to combine these two paradigms for data-driven decision making on a classical locomotion control problem.
Claire Vernade, Onno Eberhard, Martha White et al.· 0 citations
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