On-Policy-Lernen
On-Policy-Lernen ist eine Art von Verstärkungslernen (RL) method where an agent learns from the actions it takes according to its current policy. In this context, a ‘policy’ is a strategy that the agent employs to decide what actions to take in various situations. The key feature of on-policy learning is that the agent updates its policy based on the experiences gained from actions taken under that same policy.
In on-policy methods, the agent continually evaluates and improves its policy while interacting with the environment. This means that the agent’s learning is directly tied to the particular policy it follows at any given time. For example, if the agent decides to take action A in state S, it learns how good that action was based on the immediate rewards received and the future rewards expected from following the same policy.
One of the most well-known algorithms that utilizes on-policy learning is the SARSA (State-Action-Reward-State-Action) algorithm. SARSA updates the action-value function based on the action taken by the current policy, which makes it inherently on-policy. This approach contrasts with Off-Policy-Lernen, where an agent learns about one policy while following another, allowing for more flexibility in exploration strategies.
On-policy learning can be beneficial in certain environments where maintaining a consistent strategy is important. However, it can also lead to slower learning rates compared to off-policy methods, especially in complex environments where the optimale Politik ist nicht leicht erkennbar.