Reinforcement learning (RL) is an algorithmic theory for learning by experience optimal
action control. Two widely discussed problems within this field are the temporal credit …

Reinforcement learning (RL) is an algorithmic theory for learning by experience optimal
action control. Two widely discussed problems within this field are the temporal credit …

This paper discusses the notion of context transfer in reinforcement learning tasks. Context
transfer, as defined in this paper, implies knowledge transfer between source and target …

The problem of temporal credit assignment in reinforcement learning is typically solved
using algorithms based on the methods of temporal differences TD (λ). Of those, Q-learning …

The general framework of reinforcement learning has been proposed by several
researchers for both the solution of optimization problems and the realization of adaptive …

We describe how a standard reinforcement learning algorithm can be changed to include a
second contextual input that is used to modulate the learning in the original algorithm. The …

The paradigm of reinforcement learning provides an appealing framework for developing
intelligent adaptive systems. The learner interacts with a possibly unknown and stochastic …

In reinforcement learning, it is common to let an agent interact for a fixed amount of time with
its environment before resetting it and repeating the process in a series of episodes. The …

When applying reinforcement learning in domains with very large or continuous state
spaces, the experience obtained by the learning agent in the interaction with the …

Reinforcement learning suffers scalability problems due to the state space explosion and the
temporal credit assignment problem. Knowledge-based approaches have received a …