Markov Models
Autoregressive Policy Optimization for Constrained Allocation Tasks
Continuous allocation tasks are a class of problems where an agent needs to distribute a limited amount of resources over a set of entities at each time step.
Multi-Agent Coordination via Multi-Level Communication
SeqComm are guaranteed to improve monotonically and converge.
State-free Reinforcement Learning
Reinforcement learning (RL) studies the problem where an agent interacts with an unknown environment to optimize cumulative rewards/losses [Sutton and Barto, 2018].
Reinforcement Learning with L TL and ฯ-Regular Objectives via Optimality-Preserving Translation to Average Rewards Xuan-Bach Le
In this work, we study the relationship between these objectives.
Agent Planning with World Knowledge Model
Imitating humans' mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric W orld K nowledge M odel ( WKM) to facilitate agent
N-Agent Ad Hoc Teamwork
Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings.
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
Modularity is a critical design principle for both software systems and artificial intelligence (AI).
Online POMDP Planning with Anytime Deterministic Guarantees
Autonomous agents operating in real-world scenarios frequently encounter uncertainty and make decisions based on incomplete information.