Applying the learning mechanism of natural living beings to endow intelligent robots with
humanoid perception and decision-making wisdom becomes an important force to promote …

A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

In this work we aim to solve a large collection of tasks using a single reinforcement learning
agent with a single set of parameters. A key challenge is to handle the increased amount of …

This paper analyzes the characteristics and challenges of the new generation smart
dispatch systems, and proposes the framework of smart dispatch. Secondly, the …

In this paper we argue for the fundamental importance of the value distribution: the
distribution of the random return received by a reinforcement learning agent. This is in …

The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge
of building AI agents with general competency across dozens of Atari 2600 games. It …

This paper presents an actor-critic deep reinforcement learning agent with experience
replay that is stable, sample efficient, and performs remarkably well on challenging …

In this work, we take a fresh look at some old and new algorithms for off-policy, return-based
reinforcement learning. Expressing these in a common form, we derive a novel algorithm …

The goal of reinforcement learning algorithms is to estimate and/or optimise the value
function. However, unlike supervised learning, no teacher or oracle is available to provide …