Abstract—Reinforcement learning has difficulties in solving multi-agent problems because of the inefficiency of function approximation.

Abstract—Reinforcement learning has difficulties in solving multi-agent problems because of the inefficiency of function approximation.

Reinforcement learning has difficulties in solving multi-agent problems because of the inefficiency of function approximation. Sparse distributed memories ...

Bibliographic details on Adaptive Fuzzy Function Approximation for Multi-agent Reinforcement Learning.

We conclude that fuzzy Kanerva Coding with prototype tuning and adaptation can significantly improve a reinforcement learner's ability to solve large-scale ...

Meleis, Adaptive Fuzzy Function Approximation for Multi-Agent Reinforcement Learning ... Meleis, Fuzzy Kanerva-based Function Approximation for Reinforcement ...

We conclude that adaptive fuzzy. Kanerva Coding can significantly improve a reinforcement learner's ability to solve large-scale multi-agent problems.

To help approximate the unknown value function and the associated optimal strategy, actor-critic neural networks are usually utilized [14]. The actor-critic ...

Mar 17, 2023 · RL has helped with various robotic applications, such as unmanned flexible wing aircraft [18],. [19], autonomous helicopters [20], crowd aware ...

Oct 7, 2023 · This paper investigates the fuzzy approximation-based optimal consensus control problem for nonlinear multiagent systems with unknown perturbations.