← Back to all news
arXiv Machine Learning By Omar Adalat, Edwin Hamel-De le Court, Francesco Belardinelli

Learning Rational Strategies for Temporal Objectives in Multi-Agent Systems

Read the original on arXiv Machine Learning →

The paper introduces a method for rational synthesis in stochastic multi‑agent systems with unknown dynamics, where each agent has a probabilistic temporal objective expressed in Probabilistic Linear Temporal Logic on Finite Traces. It formulates per‑agent rational synthesis as finding a strategy profile that satisfies the designated agent’s objective while also constituting a stable solution under chosen equilibrium concepts such as Nash or subgame‑perfect equilibrium. The authors provide conditional convergence guarantees for tabular Nash‑Q learning, certify satisfaction with finite‑sample confidence bounds, and evaluate strategic stability via approximate best‑response checking, validating the approach on eight general‑sum concurrent stochastic games.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 31

Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games

The paper introduces aspiration-based perturbed learning automata (APLA), a payoff‑based learning scheme that incorporates an aspiration factor to reinforce action selection in distributed multi‑player games. It presents a stochastic stability analysis of APLA in positive‑utility games with noisy observations, establishing that the infinite‑dimensional Markov chain induced by the dynamics can be reduced to a finite‑dimensional one. This work extends previous results beyond potential and coordination games to generic non‑zero‑sum games, with a second part focusing on weakly acyclic games.

By Georgios C. Chasparis
More like this →
arXiv Machine Learning
Aug 24

Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models

The paper introduces BUMEX, a reinforcement learning exploration strategy that leverages a set of prior models containing the true transition kernel and reward function. By optimizing over this model set, the method derives upper and lower bounds on the Q‑function to guide exploration, providing theoretical guarantees of convergence to the optimal policy. When the model set follows a bounded‑parameter MDP structure, the optimization becomes convex, enabling finite‑time convergence under mild assumptions and demonstrating accelerated learning in simulations.

By J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
agentsreinforcement-learningbenchmarks
More like this →