Search Results for author:
Found 9 papers, 1 papers with code
Paths to Equilibrium in Normal-Form Games
In multi-agent reinforcement learning (MARL), agents repeatedly interact across time and revise their strategies as new data arrives, producing a sequence of strategy profiles.
Asynchronous Decentralized Q-Learning: Two Timescale Analysis By Persistence
In this paper, we study an asynchronous variant of the decentralized Q-learning algorithm, a recent MARL algorithm for stochastic games.
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
Stochastic games are a popular framework for studying multi-agent reinforcement learning (MARL).
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity
Our approach builds on (i) viewing quantization as a measurement kernel and thus a quantized MDP as a partially observed Markov decision process (POMDP), (ii) utilizing near optimality and convergence results of Q-learning for POMDPs, and (iii) finally, near-optimality of finite state model approximations for MDPs with weakly continuous kernels which we show to correspond to the fixed point of the constructed POMDP.
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games
In multi-agent reinforcement learning (MARL), independent learners are those that do not observe the actions of other agents in the system.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Convex Analytic Method Revisited: Further Optimality Results and Performance of Deterministic Policies in Average Cost Stochastic Control
The convex analytic method has proved to be a very versatile method for the study of infinite horizon average cost optimal stochastic control problems.
Optimization and Control 90C40, 93E20
Signaling Games for Log-Concave Distributions: Number of Bins and Properties of Equilibria
For sources with two-sided unbounded support, we prove that, for any finite number of bins, there exists a unique equilibrium.
Quantization
Information Theory
Information Theory
Quadratic Privacy-Signaling Games and the MMSE Information Bottleneck Problem for Gaussian Sources
We show that this MMSE Gaussian Information Bottleneck Problem admits a linear solution which is explicitly characterized in the paper.
Information Theory Information Theory Optimization and Control
Decentralized Q-Learning for Stochastic Teams and Games
There are only a few learning algorithms applicable to stochastic dynamic teams and games which generalize Markov decision processes to decentralized stochastic control problems involving possibly self-interested decision makers.
