Fora da Política Aprendizado por Reforço is a type of reinforcement learning where an agent learns from data generated by a different policy than the one it is currently following. This approach allows the agent to learn from various sources, including historical data or simulations, which can speed up the learning process and improve efficiency.
No aprendizado por reforço tradicional (conhecido como aprendizado on-policy), the agent learns only from the actions it takes and their consequences. However, in aprendizado off-policy, the agent can utilize experiences from past actions that might be generated by different policies, making it more versatile. This is particularly useful in scenarios where collecting novos dados é caro ou impraticável.
One of the most common algorithms used in off-policy learning is Q-learning. Q-learning enables the agent to learn the value of taking certain actions in specific states, independent of the policy used to generate that data. This flexibility allows for the integration of data from different sources, enhancing the agent’s ability to make better decisions over time.
O aprendizado off-policy também pode incorporar técnicas como amostragem de importância, which adjusts the value of the data based on the likelihood of the actions taken under the current policy compared to the behavior policy that generated the data. This adjustment helps ensure that the learning process remains stable and converges towards an optimal policy.
Overall, off-policy reinforcement learning is a powerful approach that enhances the capability of agents to learn from diverse experiences, thereby improving their performance in complex ambientes.