Cover Image

Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Morimura, Tetsuro, Ota, Kazuhiro, Abe, Kenshi, Zhang, Peinan
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Policy Gradient with Kernel Quadrature
by: Hayakawa, Satoshi, et al.
Published: (2023)

Filtered Direct Preference Optimization
by: Morimura, Tetsuro, et al.
Published: (2024)

Interaction Locality in Hierarchical Recursive Reasoning
by: Miyanishi, Yosuke, et al.
Published: (2026)

Policy Gradient for Robust Markov Decision Processes
by: Wang, Qiuhao, et al.
Published: (2024)

Return-Aligned Decision Transformer
by: Tanaka, Tsunehiko, et al.
Published: (2024)

Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
by: Bai, Qinbo, et al.
Published: (2023)

Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment
by: Jinnai, Yuu, et al.
Published: (2024)

Policy Testing in Markov Decision Processes
by: Ariu, Kaito, et al.
Published: (2025)

Optimal Decision Tree Policies for Markov Decision Processes
by: Vos, Daniël, et al.
Published: (2023)

Mean-Variance Efficient Reinforcement Learning with Applications to Dynamic Financial Investment
by: Kato, Masahiro, et al.
Published: (2020)

Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis
by: Amiri, Mohsen, et al.
Published: (2025)

Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
by: Kim, Hyunsu, et al.
Published: (2025)

SPOT: Scalable Policy Optimization with Trees for Markov Decision Processes
by: Xiong, Xuyuan, et al.
Published: (2025)

Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
by: Luo, Baiting, et al.
Published: (2024)

Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes
by: Mondal, Washim Uddin, et al.
Published: (2023)

Model-Based Minimum Bayes Risk Decoding for Text Generation
by: Jinnai, Yuu, et al.
Published: (2023)

Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
by: Jinnai, Yuu, et al.
Published: (2024)

Robust Lagrangian and Adversarial Policy Gradient for Robust Constrained Markov Decision Processes
by: Bossens, David M.
Published: (2023)

Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
by: Bennett, Andrew, et al.
Published: (2024)

Conformal Off-Policy Evaluation in Markov Decision Processes
by: Foffano, Daniele, et al.
Published: (2023)

Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processes
by: Kumar, Navdeep, et al.
Published: (2025)

Policy Regularized Distributionally Robust Markov Decision Processes with Linear Function Approximation
by: Gu, Jingwen, et al.
Published: (2025)

Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces
by: Ota, Toshihiro
Published: (2024)

Improved Monte Carlo Planning via Causal Disentanglement for Structurally-Decomposed Markov Decision Processes
by: Liu, Larkin, et al.
Published: (2024)

Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes
by: Vora, Kevin, et al.
Published: (2025)

Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs
by: Kohler, Hector, et al.
Published: (2023)

Markov Decision Processes under External Temporal Processes
by: Ayyagari, Ranga Shaarad, et al.
Published: (2023)

Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning
by: Sanokowski, Sebastian, et al.
Published: (2025)

Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
by: Moon, Sang Bin, et al.
Published: (2024)

Variance-Aware Prior-Based Tree Policies for Monte Carlo Tree Search
by: Weichart, Maximilian
Published: (2025)

Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
by: Blaser, Ethan, et al.
Published: (2026)

Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes
by: Infante, Guillermo, et al.
Published: (2021)

An Offline Risk-aware Policy Selection Method for Bayesian Markov Decision Processes
by: Angelotti, Giorgio, et al.
Published: (2021)

SPO: Sequential Monte Carlo Policy Optimisation
by: Macfarlane, Matthew V, et al.
Published: (2024)

Policy Gradient Methods for Non-Markovian Reinforcement Learning
by: Kar, Avik, et al.
Published: (2026)

1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization
by: Azeem, Muqsit, et al.
Published: (2024)

A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning
by: Banse, Adrien, et al.
Published: (2024)

On the Convergence of Modified Policy Iteration in Risk Sensitive Exponential Cost Markov Decision Processes
by: Murthy, Yashaswini, et al.
Published: (2023)

Sequential Monte Carlo for Policy Optimization in Continuous POMDPs
by: Abdulsamad, Hany, et al.
Published: (2025)

Solving Robust Markov Decision Processes: Generic, Reliable, Efficient
by: Meggendorfer, Tobias, et al.
Published: (2024)