Deterministic and stochastic policies are two approaches in the field of reinforcement learning that define how an agent makes decisions based on its current state. A deterministic policy always produces the same action given a specific state. This means that if the agent is in the same state multiple times, it will consistently choose the same action each time. For instance, if a robot is navigating a maze and encounters a sharp turn, a deterministic policy might dictate that it always turns left in that position. This predictability can be useful in environments that are stable and where outcomes are consistent.
In contrast, a stochastic policy incorporates randomness into the decision-making process. With a stochastic policy, the action chosen from a given state can vary each time, as the policy may assign a probability distribution over possible actions. This means that, under certain circumstances, the agent may choose to turn left 70% of the time and turn right 30% of the time when encountering the same sharp turn. This random behavior is beneficial in scenarios where exploration is valuable, such as in complex environments where the agent needs to discover useful strategies rather than just exploiting known information.
The choice between deterministic and stochastic policies often depends on the specific problem an agent is trying to solve. Deterministic policies can be more efficient in clear and straightforward tasks, where consistency is required. However, in more dynamic or uncertain environments, stochastic policies may provide better long-term performance through exploration. For example, in a game of chess, a deterministic policy could lead to predictable strategies, allowing an opponent to counter effectively, whereas a stochastic policy could introduce novelty, potentially confusing the opponent and allowing for more creative strategies.