Parameterized MPC exactly represents optimal policies for MDPs with future information under identified structural conditions and parameters can be learned via RL.
Model- based reinforcement learning: A survey
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This paper presents a survey of the integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps. First, we systematically cover approaches to dynamics model learning, including challenges like dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. Second, we present a systematic categorization of planning-learning integration, including aspects like: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. After these two sections, we also discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and we cover the potential benefits of model-based RL. Along the way, the survey also draws connections to several related RL fields, like hierarchical RL and transfer learning. Altogether, the survey presents a broad conceptual overview of the combination of planning and learning for MDP optimization.
years
2026 4representative citing papers
UBP2 uses ensembles of reward, dynamics, and value models to score trajectories on a unified objective of reward plus uncertainty, yielding sublinear regret bounds and higher sample efficiency on Meta-World than prior preference-based methods.
G-RSSM learns per-node dynamics in wireless ad hoc networks via graph attention and trains clustering policies through imagined rollouts, generalizing from N=50 training to larger networks.
Lifting non-conservative, actuated, and contact-constrained robot dynamics into an exactly symplectic phase-space map yields state-of-the-art out-of-distribution autoregressive rollout error at low parameter and FLOP cost.
citing papers explorer
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Solving Markov Decision Processes with Future Information via MPC
Parameterized MPC exactly represents optimal policies for MDPs with future information under identified structural conditions and parameters can be learned via RL.
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UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning
UBP2 uses ensembles of reward, dynamics, and value models to score trajectories on a unified objective of reward plus uncertainty, yielding sublinear regret bounds and higher sample efficiency on Meta-World than prior preference-based methods.
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Learning Ad Hoc Network Dynamics via Graph-Structured World Models
G-RSSM learns per-node dynamics in wireless ad hoc networks via graph attention and trains clustering policies through imagined rollouts, generalizing from N=50 training to larger networks.
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CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts
Lifting non-conservative, actuated, and contact-constrained robot dynamics into an exactly symplectic phase-space map yields state-of-the-art out-of-distribution autoregressive rollout error at low parameter and FLOP cost.