Onpolicy

On-policy reinforcement learning refers to a class of methods in which the agent learns about and improves the policy it is currently following. The data used to update the policy are generated by executing that same policy, so the learning process is tightly coupled to the behavior policy being updated.

In on-policy methods, policy evaluation and policy improvement are carried out using trajectories produced by the current policy. This means that the agent cannot easily reuse data collected under a different policy without corrections, making the approach more straightforward theoretically but often less data-efficient in practice. Common on-policy algorithms include SARSA, certain policy gradient methods such as REINFORCE, and modern actor-critic approaches like A2C and PPO in their standard on-policy forms.

On-policy methods can offer stability and simpler convergence properties in some environments because the updates are based on data that accurately reflects the policy being optimized. However, they typically require fresh data for each update and can be sensitive to exploration strategies, which can limit sample efficiency and slow learning in complex tasks.

By contrast, off-policy methods learn about a policy using data gathered from potentially different policies, enabling re-use of past experience and greater data efficiency but often at the cost of increased algorithmic and statistical complexity. Examples of off-policy approaches include Q-learning, DQN, and certain deep deterministic policy gradient variants.

In practice, the choice between on-policy and off-policy approaches depends on the task, data availability, and computational considerations. On-policy methods are commonly used when stable, reliable learning from current behavior is prioritized.