Reinforcement learning

Reinforcement learning (RL) is a subfield of Artificial intelligence (AI) that focuses on training agents to make decisions by interacting with an environment. The agent learns to achieve a goal by taking actions that maximize some notion of cumulative reward.

The concept of reinforcement learning has roots in psychology and was formally introduced into the field of machine learning in the 1980s. Recent advances in computational power and algorithms have led to significant breakthroughs in RL, making it possible to solve more complex problems. The agent is the entity that learns from interactions with the environment to achieve a goal.

The environment represents the context or situation in which the agent operates. The agent receives rewards or penalties based on the actions it takes, guiding its learning process. In model-free RL, the agent learns to make decisions without a model of the environment. In model-based RL, the agent builds a model of the environment to improve its decision-making.

Multi-agent RL involves multiple agents learning to interact in a shared environment. RL is used in robotics to train machines to perform tasks autonomously. RL algorithms have been used to train agents to play complex games like Go and Poker at a high level. In healthcare, RL can be used to personalize treatment plans based on individual patient data.

One of the key challenges in RL is balancing the need to explore new actions with the need to exploit known actions that yield high rewards. RL algorithms often require a large number of samples to learn effectively, which can be computationally expensive.