Description: Policy improvement in the context of reinforcement learning refers to the process of adjusting a policy, which is a strategy that an agent follows to make decisions in a given environment, with the aim of maximizing its expected return. This expected return can be understood as the accumulated reward that the agent can obtain over time by following that policy. Policy improvement is a fundamental component in reinforcement learning algorithms, as it allows the agent to learn from its experience and optimize its behavior based on the rewards received. This process can be carried out in various ways, such as through the exploration of new actions or the exploitation of actions that have already proven effective. Policy improvement can be implemented directly, where the policy is adjusted based on observed rewards, or indirectly, using methods such as value learning, where actions are evaluated and the policy is adjusted accordingly. In summary, policy improvement is essential for the autonomous learning of agents, enabling them to adapt and enhance their performance in complex and dynamic environments.
