How do you evaluate and improve your Reinforcement Learning agent during a competition?
Reinforcement Learning (RL) is a branch of machine learning that focuses on learning from interactions with an environment. RL agents can learn to perform complex tasks, such as playing games, controlling robots, or optimizing systems, by trial and error, reward and feedback, and exploration and exploitation. However, RL is also challenging, as it requires finding a balance between exploration and exploitation, dealing with uncertainty and partial information, and coping with complex and dynamic environments.
One way to test and improve your RL skills is to participate in RL competitions, where you can benchmark your agent against other competitors, learn from their solutions, and get feedback from the organizers. RL competitions can vary in terms of the task, the environment, the evaluation metrics, and the rules. Therefore, it is important to understand how to evaluate and improve your RL agent during a competition, and what are some of the best practices and common pitfalls to avoid.
In this article, we will cover six aspects of RL competition that you should consider when developing and testing your RL agent: