Advancements in Multi-Agent Reinforcement Learning

The field of reinforcement learning is moving towards addressing complex, real-world problems that involve multiple agents and partial observability. Researchers are developing innovative solutions to tackle challenges such as scalability, cooperation, and decision-making in dynamic environments. Notable progress is being made in the development of multi-agent reinforcement learning frameworks that can handle large-scale interactions and uncertain communication conditions. Additionally, there is a growing interest in applying reinforcement learning to practical problems such as traffic management, network routing, and game management.

Some particularly noteworthy papers in this area include: The paper on Multi-Agent Reinforcement Learning and Real-Time Decision-Making in Robotic Soccer for Virtual Environments presents a unified framework that addresses the challenges of multi-granularity tasks and large-scale agent interactions. The paper on Semi Centralized Training Decentralized Execution Architecture for Multi Agent Deep Reinforcement Learning in Traffic Signal Control introduces a novel architecture that achieves consistently superior performance in adaptive traffic signal control.

Sources

Reinforcement Learning in POMDP's via Direct Gradient Ascent

Dynamic Configuration of On-Street Parking Spaces using Multi Agent Reinforcement Learning

Multi-Agent Reinforcement Learning and Real-Time Decision-Making in Robotic Soccer for Virtual Environments

Scaling Internal-State Policy-Gradient Methods for POMDPs

A Multi-Agent, Policy-Gradient approach to Network Routing

Multi-Agent Reinforcement Learning with Communication-Constrained Priors

AI-Assisted Game Management Decisions: A Fuzzy Logic Approach to Real-Time Substituitions

Semi Centralized Training Decentralized Execution Architecture for Multi Agent Deep Reinforcement Learning in Traffic Signal Control

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