Applying reinforcement learning to plan manufacturing material handling

Applying reinforcement learning to Tetris

This paper investigates the possible application of reinforcement learning to Tetris. The author investigates the background of Tetris, and qualifies it in a mathematical context. The author discusses reinforcement learning, and considers historically successful applications of it. Finally the author discusses considerations surrounding implementation.

Reinforcement Learning in Plan

Applying Online Search Techniques to Reinforcement Learning

In reinforcement learning it is frequently necessary to resort to an approximation to the true optimal value function. Here we investigate the bene ts of online search in such cases. We examine \local" searches, where the agent performs a nite-depth lookahead search, and \global" searches, where the agent performs a search for a trajectory all the way from the current state to a goal state. The...

Applying Reinforcement Learning to RTS Games

Real-Time Strategy (RTS) games are challenging domains for AI, since it involves not only a large state space, but also dynamic actions that agents execute concurrently. This problem cannot be optimally solved through general Qlearning techniques, so we propose a solution using a Semi Markov Decision Process (SMDP). We present a time-based reward shaping technique, TRS, to speed up the learning...

VQQL. Applying Vector Quantization to Reinforcement Learning

Reinforcement learning has proven to be a set of successful techniques for finding optimal policies on uncertain and/or dynamic domains, such as the RoboCup. One of the problems on using such techniques appears with large state and action spaces, as it is the case of input information coming from the Robosoccer simulator. In this paper, we describe a new mechanism for solving the states general...