Reinforcement-learning algorithms are typically modeled as a Markov Decision Process, with an agent in an environment, as modeled in the diagram below. Image ... example of a game of chess.

An AI strategy proven adept at board games ... computer program learns to make decisions by trying different actions and receiving feedback. Such an algorithm can learn to play chess, for example ...

A chess-playing agent’s goal is to win the game ... where reinforcement learning researchers have been able to design good reward signals is growing. A more recent example is the use of ...

An experienced human player, playing white, could readily steer the game into a draw, but powerful computer ... Reinforcement learning is how AlphaZero learned to become a chess master.

Growing up, many of us were told that playing computer or video games ... learning techniques. This is partly by necessity. Go, for example, features far more possible board positions than chess ...

In a remarkably prescient 1948 report, Alan Turing – the father of modern computer ... reinforcement learning was in the board game Go. Researchers thought that Go was much harder than chess ...

Reinforcement-learning algorithms ... A familiar example of this kind of planning is move selection in board games. If a computer program is playing against a human, it needs to make moves that ...

Researchers from the AI research organization Palisade Research instructed seven large language models to play hundreds of games ... at chess. Non-reasoning LLMs use reinforcement learning to ...

Here we introduce a new approach to computer Go that uses ... by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play.