Reinforcement Learning is a powerful machine learning paradigm where an intelligent agent learns to make optimal decisions through trial and error interactions with its environment. Unlike supervised learning which learns from labeled training examples, or unsupervised learning which discovers hidden patterns in data, reinforcement learning learns from the consequences of its actions. The agent receives rewards for good actions and penalties for bad ones, gradually improving its decision-making strategy over time. This approach mimics how humans and animals learn through experience, making it particularly suitable for sequential decision-making problems.
The reinforcement learning framework consists of four essential components that work together in a continuous cycle. First, the Agent is the learner or decision-maker that observes the current state and selects actions. Second, the Environment represents the external world that the agent interacts with, including all the rules and dynamics. Third, Actions are the set of possible choices available to the agent at each step. Fourth, Rewards are the numerical feedback signals that the environment provides to guide the agent's learning. The learning process follows a cyclical pattern: the agent observes the current state, selects an action based on its current policy, receives a reward and observes the new state, then updates its knowledge to improve future decisions.
States are fundamental to reinforcement learning as they represent the agent's complete perception of the environment at any moment in time. A state contains all the relevant information the agent needs to make optimal decisions. States can be discrete, like positions on a grid, or continuous, like the angle and velocity of a pendulum. Policies are equally important as they define the agent's decision-making strategy. A policy is a mapping from states to actions, telling the agent what to do in each situation. Policies can be deterministic, always choosing the same action for a given state, or stochastic, choosing actions according to probability distributions. The mathematical notation pi of a given s represents the probability of taking action a when in state s.
Reward systems are the heart of reinforcement learning, providing the feedback mechanism that shapes agent behavior. Rewards can be immediate, received right after an action, or delayed, appearing several steps later. They can be positive to encourage certain behaviors or negative to discourage them. The key concept is cumulative return, which represents the total discounted reward an agent expects to receive from a given point in time. The discount factor gamma controls how much the agent values future rewards compared to immediate ones. When gamma equals zero, only immediate rewards matter. When gamma equals one, all future rewards are valued equally. A common choice is gamma equals zero point nine, which provides a good balance between immediate and future rewards. This mathematical framework allows agents to make decisions that optimize long-term performance rather than just immediate gains.
The learning process in reinforcement learning involves a fundamental trade-off between exploration and exploitation. Exploration means trying new actions to discover potentially better strategies, while exploitation means using the current best-known actions to maximize immediate rewards. Early in learning, agents explore more to gather information about the environment. As learning progresses, they gradually shift toward exploiting their accumulated knowledge. Key algorithms like Q-learning learn action-value functions that estimate the expected return from taking specific actions in specific states. The Q-learning update rule combines the immediate reward with the discounted maximum future value. Policy gradient methods take a different approach by directly optimizing the policy parameters using gradient ascent. Value functions, whether state values or action values, provide the foundation for making optimal decisions. Through repeated episodes of interaction, the agent's policy continuously improves, leading to better performance over time.