Monte Carlo methods and Temporal Difference (TD) learning are both approaches used in reinforcement learning to help an agent learn how to make decisions based on rewards received from the environment. The primary difference between the two lies in how they interact with the environment and update the value of states or actions. Monte Carlo methods rely on complete episodes, while TD learning updates values based on the current state and the estimated future rewards, often without waiting for a full episode to finish.
Monte Carlo methods require the agent to wait until it has completed a full episode before evaluating the value of actions or states. Once an episode concludes, the total return is calculated, and this return is used to update the value estimates. For example, if an agent is playing a game and it wins after several moves, Monte Carlo would average the rewards received throughout the episode and then use that average to update the action values. This approach can be effective in environments where episodes are long enough and the outcomes are clear, but it can be inefficient in situations where the environment is dynamic or the episodes are not well-structured.
In contrast, TD learning updates the value estimates based on the current state and immediately incorporates new information as the agent learns from its experiences. This means that the agent can learn from each step it takes, adjusting its value estimates on-the-fly. In practices like Q-learning, for example, the agent receives feedback on the immediate reward of its action and the estimate of the future reward from the next state to update its action value. This characteristic allows TD learning to be more flexible, as it can learn effectively in environments where episodes are unclear, are lengthy, or occur in continuous state spaces. Both methods have their strengths, and choosing between them can depend on the specific problem and environment at hand.