There is also a growing interest in real life applications of reinforcement learning.

Two of the common theories are action selection and reinforcement learning.

The domain has been used as a test bed in various reinforcement learning papers.

For further information on the exploration/exploitation trade off, see reinforcement learning.

An open problem in reinforcement learning is discovering hierarchical structure.

Professor Sutton is known for his significant contributions in the field of reinforcement learning.

In reinforcement learning, data are usually not given, but generated by an agent's interactions with the environment.

In many reinforcement learning problems, it is appropriate to optimize the average reward.

The reinforcement learning problem as described requires clever exploration mechanisms.

Rule fitness was based on a reinforcement learning technique.

Another method of choice is reinforcement learning, embodied in systems such as Alisp.

DeepMind is about reinforcement learning, or teaching computers to learn skills at the speed a human can.

In the older paper from 1992, the action model learning was studied as an extension of reinforcement learning.

A reinforcement learning algorithm can be used to maximize the future expected data compression progress.

A reinforcement learning agent interacts with its environment in discrete time steps.

The walking speed can be improved by means of reinforcement learning because there are only a few parameters in this scheme.

Confusion and reinforcement learning in experimental public goods games.

The trade-off between exploration and exploitation is also faced in reinforcement learning.

Areas to investigate are behaviour-based robotics, reinforcement learning and many others.

This is known as the exploitation vs. exploration tradeoff in reinforcement learning.

I am a PhD student working in the field of reinforcement learning and robotics.

This suggests that the area is the "pleasure center" of the brain and is involved in reinforcement learning.

In particular, other forms of reinforcement learning can be used for learning robot controllers.

The goal of a reinforcement learning agent is to collect as much reward as possible.

One such class of algorithms are the so-called policy-gradient methods from reinforcement learning.