Effective reinforcement learning requires a careful balance between exploring the environment to find better strategies and exploiting current knowledge for immediate reward. Basic exploration methods, such as $\epsilon$-greedy, often fall short in complex scenarios involving large state spaces or infrequent feedback. This chapter presents advanced exploration techniques that enable more efficient and directed discovery.

You will examine strategies rooted in managing uncertainty, including Upper Confidence Bound (UCB) methods and Thompson Sampling. We will also study count-based approaches that incentivize visiting less familiar states and intrinsic motivation techniques where agents generate internal rewards based on prediction errors (like ICM), state novelty (like RND), or information gain. Finally, the use of parameter space noise for exploration will be discussed. Understanding these methods will provide you with tools to design agents capable of tackling difficult exploration challenges.