Recently, gaming companies have become interested in using reinforcement learning and other machine learning techniques in game development.

Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules.

As machines learn to play old Atari games like Space Invaders, Video Pinball, and Breakout, they're also learning to navigate the real world.

Deep Reinforcement Learning: An approach that integrates deep learning with reinforcement learning, enabling agents to process high-dimensional inputs and learn optimal actions in complex tasks.

An AI strategy proven adept at board games like Chess and Go, reinforcement learning, has now been adapted for a powerful protein design program. The results show that reinforcement learning can ...

UC Berkeley recently released a video demonstrating its latest creation, the Humanoid Table TEnnis Robot (HITTER), playing a game of table tennis with human beings. The robot showcased exemplary ...

Reinforcement learning’s key challenge is to plan the simulation environment, which relies heavily on the task to be performed. When trained in Chess, Go, or Atari games, the simulation ...

AI algorithms for deep-reinforcement learning have demonstrated the ability to learn at very high levels in constrained domains.

In this paper we revise Reinforcement Learning and adaptiveness in Multi-Agent Systems from an Evolutionary Game Theoretic perspective. More precisely we show there is a triangular relation between ...

Reinforcement learning (RL) is a powerful type of AI technology that can learn strategies to optimally control large, complex systems.