Adaptive State Space Formation Method for Reinforcement Learning.

Adaptive State-Space Quantisation and Multi-Task Reinforcement Learning Using . . .

Indirect Reinforcement Learning with Adaptive State Space Partitions

Model-based reinforcement learning can be applied to problems with continuous state spaces by discretizing the spaces with crisp or fuzzy partitions. The manual definition of suitable partitions, however, is often not trivial, since fine partitions lead to a high number of states and thus complex discrete problems, whereas coarse partitions can be unsuitable for the representation of the optima...

Automatic Adaptive Space Segmentation for Reinforcement Learning

We tested a single pendulum simulation and observed the influence of several situation space segmentation types in reinforcement learning processes in order to propose a new adaptive automation for situation space segmentation. Its segmentation is performed by the Contraction Algorithm and the Cell Division Approach. Also, its automation is performed by “entropy,” which is defined on action val...

State Space Reduction For Hierarchical Reinforcement Learning

This paper provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, to construct a partition space that has a smaller number of states than the original MDP. As a result, learning policies on the partition space should be faster than on the original state space. The technique p...

Multiagent Reinforcement Learning with Adaptive State Focus

In realistic multiagent systems, learning on the basis of complete state information is not feasible. We introduce adaptive state focus Q-learning, a class of methods derived from Qlearning that start learning with only the state information that is strictly necessary for a single agent to perform the task, and that monitor the convergence of learning. If lack of convergence is detected, the le...