Another way to promote long-term learning is to use intrinsic motivation techniques. These techniques involve providing the agent with internal rewards based on its own curiosity or exploration. By encouraging the agent to explore new states and actions, intrinsic motivation can help the agent discover more robust and optimal strategies over time. This can be particularly useful in scenarios where the external reward signal is sparse or delayed.

Additionally, designing reward functions that consider the potential for future rewards can also promote long-term learning. Rather than solely focusing on immediate rewards, the reward function can take into account the expected future rewards that may arise from a particular state or action. This encourages the agent to prioritize actions that have a higher potential for long-term success.

It's worth mentioning that designing reward functions for long-term learning is still an active area of research in reinforcement learning, and there is no one-size-fits-all solution. Experimentation and adaptation are essential in finding reward functions that effectively promote long-term learning in specific domains.

One approach to designing reward functions for long-term learning in reinforcement learning systems is to incorporate shaping rewards. Shaping rewards provide additional feedback to guide the learning process towards achieving long-term goals. These rewards can be designed to encourage the agent to explore and discover different states and actions that lead to better long-term outcomes. For example, in a game, shaping rewards can be used to reward the agent for achieving intermediate goals or making progress towards the main objective.