Reinforcement learning (RL) addresses delayed rewards by using techniques that allow an agent to understand and associate actions with outcomes that occur after some time. This is crucial because in many real-world scenarios, the result of an action may not be immediately visible. To manage this, RL uses a method called temporal credit assignment, which helps to determine how much of a future reward can be attributed to earlier actions. This enables the agent to optimize its behavior over time, even when feedback is not instantaneous.
A common approach in reinforcement learning is the use of discount factors. The agent updates its value estimations of future rewards by applying a discount rate to rewards obtained in the future, weighing immediate rewards more heavily than distant ones. For example, if an agent receives a reward after multiple steps, the value assigned to that reward is reduced based on how far in the future it was received. This helps the agent balance immediate gains versus long-term outcomes effectively.
Another important technique is the use of algorithms like Q-learning and SARSA, which update the value estimates based on the rewards received during an episode. For instance, in an environment where an agent learns to navigate a maze, it might not receive a reward until it reaches the exit, which could take many actions. Through the learning process, the agent will refine its policy—mapping states to actions—based on the cumulative rewards it receives over time, effectively tracing back through its earlier actions to understand their contribution to the delayed reward. Thus, reinforcement learning equips agents with the tools to learn from delayed feedback through thoughtful value assessment and policy improvement strategies.