Value-based reinforcement learning (RL) methods are techniques that focus on estimating the value of states or actions in order to determine the best course of action for an agent in a given environment. These methods help agents learn optimal policies by evaluating the expected long-term rewards associated with different actions, which is crucial in the context of controlling underwater robots where decision-making is often complex and uncertain. By leveraging value functions, these methods enable efficient exploration and exploitation strategies that improve performance in dynamic underwater scenarios.
congrats on reading the definition of value-based rl methods. now let's actually learn it.