In this article we introduce the Arcade Learning Environment (ALE): both a challenge
problem and a platform and methodology for evaluating the development of general, domain …

The Arcade Learning Environment (ALE) is a popular platform for evaluating reinforcement
learning agents. Much of the appeal comes from the fact that Atari games demonstrate …

Atari games have been a long-standing benchmark in the reinforcement learning (RL)
community for the past decade. This benchmark was proposed to test general competency …

The combination of modern Reinforcement Learning and Deep Learning approaches holds
the promise of making significant progress on challenging applications requiring both rich …

This paper introduces the PettingZoo library and the accompanying Agent Environment
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …

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 …

We show that setting a reasonable frame skip can be critical to the performance of agents
learning to play Atari 2600 games. In all of the six games in our experiments, frame skip is a …

This paper describes the Mario AI benchmark, a game-based benchmark for reinforcement
learning algorithms and game AI techniques developed by the authors. The benchmark is …

In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent,
whose design is fixed, to maximize some notion of cumulative reward. The design of the …

This paper investigates whether learning contingency-awareness and controllable aspects
of an environment can lead to better exploration in reinforcement learning. To investigate …