Reinforcement learning is a type of machine learning that involves an agent learning to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties for its actions, and its goal is to learn a policy that maximizes the cumulative reward over time.
The RL process involves:
The key insight of RL is that the agent learns through trial and error, without requiring explicit supervision or labeled data. By interacting with the environment and receiving feedback, the agent can learn to make optimal decisions in complex, uncertain situations.
At our institute, research on reinforcement learning primarily focuses on the following goals:
These goals are important for developing reinforcement learning agents that can operate effectively in real-world environments, where data may be limited, and interpretability is crucial for trust and safety. Achieving these goals can lead to more robust, flexible, and autonomous agents.
