Reinforcement Learning

Reinforcement Learning (RL) trains an autonomous agent to select optimal actions within an environment, maximizing its cumulative reward signal through continuous trial-and-error.

RL is a decision-making framework: an agent learns an optimal policy by interacting with a dynamic environment (modeled as a Markov Decision Process or MDP). The agent executes an action, receives a new state, and gets a scalar reward (positive or negative). This trial-and-error loop drives the agent to maximize the total long-term reward. This core mechanism enabled DeepMind's AlphaGo to master the game of Go and is critical for autonomous vehicles navigating complex traffic scenarios. Key algorithms like Q-Learning, Policy Gradients, and Actor-Critic methods define the agent's strategy for balancing exploration (trying new actions) and exploitation (using known high-reward actions).

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