Description: A non-deterministic policy in the context of reinforcement learning refers to an approach that assigns a probability distribution over the possible actions an agent can take in a given state. Unlike a deterministic policy, which selects a specific action for each state, the non-deterministic policy allows the agent to explore different actions with some randomness. This is crucial in environments where exploration is necessary to discover optimal strategies. The randomness in action selection helps prevent the agent from getting stuck in suboptimal solutions, encouraging a broader exploration of the solution space. Non-deterministic policies are particularly useful in situations where the environment is dynamic or uncertain, as they allow the agent to adapt to changes and learn from past experiences. In summary, this type of policy is fundamental for effective learning in complex environments where variability and uncertainty are the norm.
