Description: Policy smoothing is a technique used in the field of reinforcement learning that aims to make an agent’s policy less sensitive to small variations in the environment. In this context, a ‘policy’ refers to the strategy an agent follows to decide its actions based on the states of the environment. Smoothing is implemented to prevent the agent from reacting excessively to minor changes, which could lead to erratic or inefficient behavior. By applying smoothing, a more robust and stable policy is sought, allowing the agent to better generalize its learning and adapt to similar situations without losing effectiveness. This technique can be achieved through methods such as regularization, where abrupt changes in the policy are penalized, or through averaging techniques that integrate information from multiple training episodes. In summary, policy smoothing is essential for improving the stability and effectiveness of reinforcement learning, enabling agents to learn more effectively in dynamic and complex environments.
