Related papers: Diverse Behavior Is What Game AI Needs: Generating…

This paper has been withdrawn by the author due to extremely unscientific errors.

This paper has been withdrawn by the author.

This paper has been withdrawn by the author, due an error in claim 1.

The paper has been withdrawn

This paper has been withdrawn by the author because it has been substantially modified.

This paper has been withdrawn by the author: it was a too preliminary version.

This paper has been withdrawn.

This paper has been withdrawn

This paper has been withdrawn.

Reinforcement learning has exceeded human-level performance in game playing AI with deep learning methods according to the experiments from DeepMind on Go and Atari games. Deep learning solves high dimension input problems which stop the…

This paper has been withdrawn.

This paper has been withdrawn.

This paper is wrong and is therefore withdrawn.

This paper has been withdrawn

This paper has been withdrawn by the corresponding author because the newest version is now published in Journal of Discrete Algorithms.

This paper has been withdrawn by the author, since the author does not have enough time to answer every questions on this result.

This paper has been withdrawn.

We present two variants of a multi-agent reinforcement learning algorithm based on evolutionary game theoretic considerations. The intentional simplicity of one variant enables us to prove results on its relationship to a system of ordinary…

Training AI with strong and rich strategies in multi-agent environments remains an important research topic in Deep Reinforcement Learning (DRL). The AI's strength is closely related to its diversity of strategies, and this relationship can…

This textbook covers principles behind main modern deep reinforcement learning algorithms that achieved breakthrough results in many domains from game AI to robotics. All required theory is explained with proofs using unified notation and…