A deterministic policy in reinforcement learning (RL) is a policy where the agent always takes the same action in a given state. There is no randomness involved, and the action chosen is fixed based on the current state. For example, a deterministic policy might instruct an agent to always move forward in a particular state, regardless of the context.
A stochastic policy, on the other hand, introduces randomness in the decision-making process. In this case, the agent does not always take the same action in a given state; instead, it chooses an action based on a probability distribution. For example, a stochastic policy might have a 70% chance of moving forward and a 30% chance of turning left in a given state.
The choice between deterministic and stochastic policies depends on the problem being solved. Stochastic policies are often useful in environments where exploration is important, or where uncertainty exists in the environment, while deterministic policies might be better for environments where consistency and predictability are needed.