Cover Image

Towards Principled Unsupervised Multi-Agent Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zamboni, Riccardo, Mutti, Mirco, Restelli, Marcello
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

From Parameters to Behaviors: Unsupervised Compression of the Policy Space
by: Tenedini, Davide, et al.
Published: (2025)

Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
by: Fraschini, Andrea, et al.
Published: (2026)

How to Explore with Belief: State Entropy Maximization in POMDPs
by: Zamboni, Riccardo, et al.
Published: (2024)

Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story
by: De Paola, Vincenzo, et al.
Published: (2025)

The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough
by: Zamboni, Riccardo, et al.
Published: (2024)

K-Myriad: Jump-starting reinforcement learning with unsupervised parallel agents
by: De Paola, Vincenzo, et al.
Published: (2026)

Scalable Multi-Agent Offline Reinforcement Learning and the Role of Information
by: Zamboni, Riccardo, et al.
Published: (2025)

Inverse Reinforcement Learning with Sub-optimal Experts
by: Poiani, Riccardo, et al.
Published: (2024)

Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
by: Mutti, Mirco, et al.
Published: (2023)

Building surrogate models using trajectories of agents trained by Reinforcement Learning
by: Cestero, Julen, et al.
Published: (2025)

Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs
by: Maran, Davide, et al.
Published: (2024)

How Log-Barrier Helps Exploration in Policy Optimization
by: Cesani, Leonardo, et al.
Published: (2026)

Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
by: Cestero, Julen, et al.
Published: (2025)

Do Agents Dream of Electric Sheep?: Improving Generalization in Reinforcement Learning through Generative Learning
by: Franceschelli, Giorgio, et al.
Published: (2024)

Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems
by: Dasgupta, Nayana, et al.
Published: (2023)

A Theoretical Framework for Partially Observed Reward-States in RLHF
by: Kausik, Chinmaya, et al.
Published: (2024)

Trust-based Consensus in Multi-Agent Reinforcement Learning Systems
by: Fung, Ho Long, et al.
Published: (2022)

Heterogeneous Knowledge for Augmented Modular Reinforcement Learning
by: Wolf, Lorenz, et al.
Published: (2023)

Statistical Analysis of Policy Space Compression Problem
by: Molaei, Majid, et al.
Published: (2024)

Test-Time Regret Minimization in Meta Reinforcement Learning
by: Mutti, Mirco, et al.
Published: (2024)

Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
by: Franceschelli, Giorgio, et al.
Published: (2023)

CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-Making
by: Minelli, Giovanni, et al.
Published: (2023)

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
by: Cestero, Julen, et al.
Published: (2025)

On Distributional Reinforcement Learning in Chaotic Dynamical Systems
by: Rudd-Jones, James, et al.
Published: (2026)

Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control
by: De Monte, Riccardo, et al.
Published: (2026)

Towards Fault Tolerance in Multi-Agent Reinforcement Learning
by: Shi, Yuchen, et al.
Published: (2024)

Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
by: Darvariu, Victor-Alexandru, et al.
Published: (2024)

Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
by: Salaorni, Davide, et al.
Published: (2025)

Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery
by: Darvariu, Victor-Alexandru, et al.
Published: (2023)

Impoola: The Power of Average Pooling for Image-Based Deep Reinforcement Learning
by: Trumpp, Raphael, et al.
Published: (2025)

Exploratory Diffusion Model for Unsupervised Reinforcement Learning
by: Ying, Chengyang, et al.
Published: (2025)

Information-Theoretic State Variable Selection for Reinforcement Learning
by: Westphal, Charles, et al.
Published: (2024)

Parameterized Projected Bellman Operator
by: Vincent, Théo, et al.
Published: (2023)

Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning
by: Hugessen, Adriana, et al.
Published: (2024)

Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
by: Liu, Xuanyan, et al.
Published: (2026)

No-Regret Reinforcement Learning in Smooth MDPs
by: Maran, Davide, et al.
Published: (2024)

$K$-Level Policy Gradients for Multi-Agent Reinforcement Learning
by: Reddi, Aryaman, et al.
Published: (2025)

PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
by: Shaik, Thanveer, et al.
Published: (2023)

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
by: Zhu, Ting, et al.
Published: (2024)

Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
by: Li, Simin, et al.
Published: (2023)