Reinforcement Learning is a machine learning paradigm where agents learn to make decisions through trial and error interactions with an environment. Unlike supervised learning which uses labeled data, reinforcement learning learns from rewards and punishments. The agent observes the environment's state, takes an action, receives a reward, and uses this feedback to improve its decision-making over time.
The reinforcement learning framework consists of four fundamental components that interact in a continuous cycle. The Agent is the learner that makes decisions. The Environment is the world the agent operates in. Actions are the choices available to the agent. Rewards are feedback signals that guide learning. The cycle works as follows: the agent observes the current state from the environment, selects an action based on its policy, receives a reward and new state from the environment, then updates its knowledge to improve future decisions.
The reinforcement learning process is fundamentally about trial and error learning. Agents start with random actions and gradually improve through experience. Initially, performance is low as the agent explores randomly. Over time, the agent learns which actions lead to better rewards and performance improves. A key challenge is balancing exploration of new actions versus exploitation of known good actions. The agent must explore enough to discover optimal strategies while exploiting current knowledge to maximize rewards. Through this process, the agent evolves from a random policy to an optimal policy that maximizes long-term rewards.
Reinforcement learning algorithms fall into three main categories. Value-based methods like Q-Learning learn to estimate the value of taking specific actions in given states. They build Q-tables or Q-functions that map state-action pairs to expected rewards. Policy-based methods like Policy Gradient learn policies directly by optimizing the probability distribution over actions. They adjust action probabilities to maximize expected rewards. Actor-Critic methods combine both approaches, using an actor network to learn the policy and a critic network to learn value functions. This combination often provides more stable and efficient learning than using either approach alone.
Let's see reinforcement learning in action with a grid-world navigation problem. The agent starts at position S and must reach goal G while avoiding obstacles marked with X. The agent receives a large positive reward for reaching the goal, negative rewards for hitting obstacles, and small negative rewards for each step to encourage efficiency. Initially, the agent explores randomly, trying different paths and learning from the consequences. Through trial and error, it discovers which actions lead to rewards and which lead to penalties. Over time, the agent builds up value estimates for each state and learns an optimal policy that finds the shortest safe path to the goal.