Fuzzy and Tile Coding Function Approximation in Agent Coevolution

Function Approximation Using Tile and Kanerva Coding For Multi-Agent Systems

Function approximation can improve the ability of a reinforcement learner. Tile coding and Kanerva coding are two classical methods for implementing function approximation, but these methods may give poor performance when applied to large-scale, high-dimensional instances. In the paper, we evaluate a collection of hard instances of the predator-prey pursuit problem, a classic multi-agent reinfo...

Fuzzy and tile coding approximation techniques for coevolution in reinforcement learning

A New Approach for Value Function Approximation Based on Automatic State Partition

Value function is usually used to deal with the reinforcement learning problems. In large or even continuous states, function approximation must be used to represent value function. Much of the current work carried out, however, has to design the structure of function approximation in advanced which cannot be adjusted during learning. In this paper, we propose a novel function approximation cal...

Tile Coding Based on Hyperplane Tiles

In large and continuous state-action spaces reinforcement learning heavily relies on function approximation techniques. Tile coding is a well-known function approximator that has been successfully applied to many reinforcement learning tasks. In this paper we introduce the hyperplane tile coding, in which the usual tiles are replaced by parameterized hyperplanes that approximate the action-valu...

Fuzzy Kanerva-based function approximation for reinforcement learning

Radial Basis Functions and Kanerva Coding can give poor performance when applied to large-scale multi-agent systems. In this paper, we attempt to solve a collection of predator-prey pursuit instances and argue that the poor performance is caused by frequent prototype collisions. We show that dynamic prototype allocation and adaptation can give better results by reducing these collisions. We the...