The learning rate in reinforcement learning (RL) is a hyperparameter that determines how much the agent updates its knowledge or value estimates based on new experiences. It controls the size of the step the agent takes when adjusting its action-value estimates (Q-values) or policy. A high learning rate means that the agent will quickly incorporate new information, while a low learning rate means the agent will update its values more gradually.
The learning rate is important for ensuring that the agent learns effectively without overshooting or getting stuck. If the learning rate is too high, the agent might update its values too drastically, leading to instability or poor performance. If it is too low, learning can become slow, and the agent might take too long to converge to an optimal policy.
In practical terms, the learning rate determines how much the agent adjusts its estimates when it receives feedback. For example, in Q-learning, the learning rate is used to update the Q-values after each action.