Reinforcement Learning
Reinforcement learning, explained end to end: how agents learn to act through trial, error and reward signals, and where the field is heading — from self-play training and algorithm advances to real deployments in games, robotics and language-model alignment.
Reinforcement learning teaches machines to decide by interacting with an environment: an agent tries actions, gets rewards or penalties to refine its strategy. It powered AlphaGo's win over the Go world champion, Ataraxos's run against top Stratego players, and the alignment training behind large language models. This topic tracks algorithms, self-play techniques, reward modeling, and real deployments in robotics, games and AI assistants.
