This paper presents a system that transfers the results of prior learning to speed up
reinforcement learning in a changing world. Often, even when the change to the world is …

This paper discusses a system that accelerates reinforcement learning by using transfer
from related tasks. Without such transfer, even if two tasks are very similar at some abstract …

Temporally extended actions (eg, macro actions) have proven very useful for speeding up
learning, ensuring robustness and building prior knowledge into AI systems. The options …

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 …

A common problem in reinforcement learning is finding a balance between exploration
(attempting to discover new features about the world by a selecting sub-optimal action) and …

In this paper we propose a method for solving reinforcement learning problems in non-
stationary environments. The basic idea is to create and simultaneously update multiple …

We aim to accelerate learning processes in reinforcement learning by transfer learning. Its
concept is that knowledge to solve similar tasks accelerates a learning process of a target …

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 …

Reinforcement learning (Kaelbling et al., 1996; Sutton & Barto, 1998) is a machine learning
technique that automatically acquires a good action policy, ie a mapping from the current …

The application of reinforcement learning to control problems has received considerable
attention in the last few years And86, Bar89, Sut84]. In general there are two principles to …