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Found 17 papers, 1 papers with code
Model approximation in MDPs with unbounded per-step cost
How well does an optimal policy $\hat{\pi}^{\star}$ of the approximate model perform when used in the original model $\mathcal{M}$?
Regret Analysis of the Posterior Sampling-based Learning Algorithm for Episodic POMDPs
We propose a Posterior Sampling-based reinforcement learning algorithm for POMDPs (PS4POMDPs), which is much simpler and more implementable compared to state-of-the-art optimism-based online learning algorithms for POMDPs.
Conditional Kernel Imitation Learning for Continuous State Environments
Classical methods such as behavioral cloning and inverse reinforcement learning are highly sensitive to estimation errors, a problem that is particularly acute in continuous state space problems.
Optimal Control of Logically Constrained Partially Observable and Multi-Agent Markov Decision Processes
In this paper, we first introduce an optimal control theory for partially observable Markov decision processes (POMDPs) with finite linear temporal logic constraints.
A Novel Point-based Algorithm for Multi-agent Control Using the Common Information Approach
The Common Information (CI) approach provides a systematic way to transform a multi-agent stochastic control problem to a single-agent partially observed Markov decision problem (POMDP) called the coordinator's POMDP.
Optimal Communication and Control Strategies for a Multi-Agent System in the Presence of an Adversary
We consider a multi-agent system in which a decentralized team of agents controls a stochastic system in the presence of an adversary.
Optimal Control of Partially Observable Markov Decision Processes with Finite Linear Temporal Logic Constraints
Autonomous agents often operate in scenarios where the state is partially observed.
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent
In this paper, we propose Posterior Sampling Reinforcement Learning for Zero-sum Stochastic Games (PSRL-ZSG), the first online learning algorithm that achieves Bayesian regret bound of $O(HS\sqrt{AT})$ in the infinite-horizon zero-sum stochastic games with average-reward criterion.
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems
The regret bound of the algorithm was derived under a technical assumption on the induced norm of the closed loop system.
Scalable regret for learning to control network-coupled subsystems with unknown dynamics
We consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems connected over a network.
Online Learning for Unknown Partially Observable MDPs
Learning optimal controllers for POMDPs when the model is unknown is harder.
Common Information Belief based Dynamic Programs for Stochastic Zero-sum Games with Competing Teams
For this general model, we provide bounds on the upper (min-max) and lower (max-min) values of the game.
Multiagent Systems Systems and Control Systems and Control
Thompson sampling for linear quadratic mean-field teams
We consider optimal control of an unknown multi-agent linear quadratic (LQ) system where the dynamics and the cost are coupled across the agents through the mean-field (i. e., empirical mean) of the states and controls.
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems
This allows the agents to achieve a regret within $O(\sqrt{T})$ of the regret of the auxiliary single-agent problem.
Sequential Experiment Design for Hypothesis Verification
In the exploration phase, selection of experiments is such that a moderate level of confidence on the true hypothesis is achieved.
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
This regret bound matches the best available bound for weakly communicating MDPs.
Decentralized Stochastic Control with Partial History Sharing: A Common Information Approach
A general model of decentralized stochastic control called partial history sharing information structure is presented.
Systems and Control Optimization and Control
