Reinforcement learning deals with learning optimal or near optimal policies while interacting
with an external environment. The applicability of reinforcement learning has been limited by …

Reinforcement learning has been repeatedly suggested as good candidate for learning in
robotics. However, the large search spaces normally occurring robotics and expensive …

Reinforcement learning deals with learning optimal or near optimal policies while interacting
with the environment. Application domains with many continuous variables are difficult to …

In reinforcement learning, an agent tries to learn a policy, ie, how to select an action in a
given state of the environment, so that it maximizes the total amount of reward it receives …

Much research in Reinforcement Learning (RL) has focused on learning algorithms and
generalization using simple representation languages for states and actions. Recently, there …

Relational reinforcement learning is presented, a learning technique that combines
reinforcement learning with relational learning or inductive logic programming. Due to the …

Relational reinforcement learning is presented, a learning technique that combines
reinforcement learning with relational learning or inductive logic programming. Due to the …

This paper proposes a reinforcement learning architecture containing multiple “experts”,
each of which is a specialist in a different region in the overall state space. The central idea …

Current reinforcement learning (Kaelbling et al., 1996)(RL) research is largely concerned
with structure and generalization in sequential learning tasks1. For example, hierarchical …

In reinforcement learning problems, an agent has the task of learning a good or optimal
strategy from interaction with his environment. At the start of the learning task, the agent …