In reinforcement learning, the difference between on-policy and off-policy methods lies in how they use data to update the agent’s policy. On-policy methods learn exclusively from experiences generated by the agent’s current policy. This means the actions taken to explore the environment and the actions used to improve the policy are governed by the same strategy. Off-policy methods, by contrast, can learn from experiences generated by a different policy (e.g., an older version of the agent or a completely separate behavior policy). This decouples exploration (gathering data) from exploitation (improving the policy), offering more flexibility in how data is reused.
A key example of on-policy learning is the SARSA algorithm. SARSA updates the agent’s Q-values (estimates of action quality) based on the current policy’s next action. For instance, if the agent uses an epsilon-greedy strategy (exploring randomly with probability epsilon), SARSA incorporates the actual action the policy would take in the next state. This tight coupling ensures updates align with the agent’s current behavior but limits data reuse. Off-policy methods like Q-learning take a different approach. Q-learning updates Q-values using the maximum estimated value of the next state, regardless of the action the current policy would take. This allows Q-learning to learn from data generated by exploratory or outdated policies, making it possible to reuse past experiences (e.g., stored in a replay buffer) more efficiently.
The trade-offs between these approaches are significant. On-policy methods, like Proximal Policy Optimization (PPO), often require fresh data from the current policy, which can be computationally expensive. However, they tend to be more stable because updates are directly tied to the agent’s behavior. Off-policy methods, like Deep Q-Networks (DQN), excel at sample efficiency by reusing historical data but may face challenges like instability due to outdated or mismatched data. For example, if a DQN agent uses a replay buffer filled with random exploration data, it might overestimate Q-values if the environment dynamics have changed. Developers often choose on-policy methods for tasks requiring precise control (e.g., robotics) and off-policy methods for environments where data collection is costly (e.g., training agents in simulations).