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Found 13 papers, 0 papers with code
O3D: Offline Data-driven Discovery and Distillation for Sequential Decision-Making with Large Language Models
Recent advancements in large language models (LLMs) have exhibited promising performance in solving sequential decision-making problems.
On-Robot Bayesian Reinforcement Learning for POMDPs
Bayesian reinforcement learning (BRL), thanks to its sample efficiency and ability to exploit prior knowledge, is uniquely positioned as such a solution method.
Sequential Fair Resource Allocation under a Markov Decision Process Framework
We propose a new algorithm, SAFFE, that makes fair allocations with respect to the entire demands revealed over the horizon by accounting for expected future demands at each arrival time.
Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability
Empirical results demonstrate the superiority of our approaches in large multi-agent problems and validate the effectiveness of our algorithms for learning high-quality and asynchronous solutions with macro-actions.
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination reliably.
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning
Centralized Training for Decentralized Execution, where training is done in a centralized offline fashion, has become a popular solution paradigm in Multi-Agent Reinforcement Learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning
By using this local critic, each agent calculates a baseline to reduce variance on its policy gradient estimation, which results in an expected advantage action-value over other agents' choices that implicitly improves credit assignment.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+2
Asynchronous Multi-Agent Actor-Critic with Macro-Actions
Many realistic multi-agent problems naturally require agents to be capable of performing asynchronously without waiting for other agents to terminate (e. g., multi-robot domains).
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning
Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity in the multi-agent reinforcement learning community.
Adaptive directional Haar tight framelets on bounded domains for digraph signal representations
Based on hierarchical partitions, we provide the construction of Haar-type tight framelets on any compact set $K\subseteq \mathbb{R}^d$.
Macro-Action-Based Deep Multi-Agent Reinforcement Learning
In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durations.
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net
In many real-world multi-robot tasks, high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control.
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
The practicality of existing works addressing this challenge is limited to only small-scale synchronous decision-making scenarios or a single agent planning its best response against a single adversary with fixed, procedurally characterized strategies.
