The paper introduces a robust variant of Monte Carlo Tree Search that addresses ambiguities in transition dynamics and reward distributions, bridging the gap between simulation-based planning and real-world deployment. It incorporates a robust power mean backup operator and exploration bonuses to guarantee finite-sample convergence at every node, achieving an ≠O(n−1/2) convergence rate for root value estimation comparable to standard MCTS. Empirical results demonstrate robust performance in planning tasks even under significant model mismatches.
arXiv:2609.06489v1 Announce Type: cross Abstract: Monte Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapt...
arXiv:2610. 23565v1 Announce Type: new Abstract: Monte-Carlo Tree Search (MCTS) typically stores only scalar mean estimates, which makes it poorly suited for tail-risk objectives such as conditional value-at-risk (CVaR).
arXiv:2607. 05359v1 Announce Type: new Abstract: Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding.
arXiv:2605.08982v4 Announce Type: replace Abstract: Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement and action selection in Reinforcement Learning. Due to its sequenti...
Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding. Tree-based search methods such as Monte Carlo Tree Search (MCTS) remain popular, but their branching structure can require sampling budgets that grow exponentially with lookahead depth in the worst case.
arXiv:2607. 09298v1 Announce Type: cross Abstract: We study general-utility Markov decision processes (GUMDPs) with risk-aware objectives.
arXiv:2610.27575v1 Announce Type: new Abstract: Model-based reinforcement learning in partially observable environments requires inferring hidden states, learning latent dynamics, and planning under...
arXiv:2605. 29032v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss.
The article argues that Monte Carlo Tree Search (MCTS) and every‑visit Monte Carlo (MC) control are essentially the same method, differing only in terminology and presentation. It shows that MCTS’s four stages—selection, expansion, simulation, and backup—can be reduced to two core operations: sampling trajectories under the current policy and performing every‑visit MC updates. The note aims to make this equivalence explicit and easier to recognize.
arXiv:2601. 19612v3 Announce Type: replace-cross Abstract: Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.
arXiv:2610.15461v1 Announce Type: new Abstract: MDP planners often rely on nominal transition models that may differ from the true dynamics. Robust MDP planning accounts for this uncertainty through...
arXiv:2608. 09335v1 Announce Type: new Abstract: Multistage stochastic model predictive control (MPC) handles uncertainty by optimizing over a scenario tree, a finite branching approximation of future outcomes constructed from sampled forecasts.