On-policy and off-policy learning are two distinct approaches in reinforcement learning that determine how an agent learns from its experiences. The primary difference lies in how the learning process utilizes actions taken by the agent. In on-policy learning, the agent learns based on the actions it actually takes while following its current policy. This means the agent updates its understanding of the environment based on the feedback received from the specific actions it chose to execute. A well-known example of on-policy learning is the SARSA (State-Action-Reward-State-Action) algorithm, where the agent observes a sequence of states, actions, and rewards, and learns directly from the consequences of its own actions.
In contrast, off-policy learning allows the agent to learn from actions that are not necessarily taken by its current policy. This means that the agent can learn from historical experiences or actions that are crafted by a different policy. This flexibility enables the agent to utilize experiences collected from various sources, including previous versions of itself or even other agents. The Q-learning algorithm is a classic example of off-policy learning. Here, the agent can learn the value of an action in a state regardless of whether that action was actually taken, which is useful for evaluating different strategies without having to follow them directly.
The choice between on-policy and off-policy learning can affect the efficiency and effectiveness of the learning process. On-policy learning tends to be more stable since the agent consistently improves based on its own experiences. However, this can limit exploration since the agent can only learn from the actions it takes. On the other hand, off-policy learning enhances the exploration capability, allowing the agent to refine its policy based on a wider range of experiences. This can be particularly advantageous in environments with sparse rewards, where gathering information through varied actions can lead to quicker learning. Developers should choose the approach that best aligns with the goals of their projects, considering factors like exploration needs and stability requirements.