Description: A non-stationary policy in the context of reinforcement learning refers to a strategy that evolves and adapts over time, rather than remaining fixed. This means that the decisions made by an agent in a dynamic environment can change based on accumulated experience and variations in the environment. Non-stationary policies are crucial in situations where environmental conditions may vary, requiring the agent to adjust its behavior to maximize long-term rewards. This adaptability allows agents to learn from their interactions and improve their performance as they gain more information. Non-stationary policies can be implemented through techniques such as online learning, where the agent continuously updates its knowledge and makes decisions based on the most recent information. In summary, a non-stationary policy is fundamental for effective learning in complex and changing environments, as it allows agents to respond flexibly to new situations and challenges.
