A tutorial about how to fix one of the most misunderstood strategies: Exploration vs Exploitation

In this tutorial:

• You will understand that Exploration vs Exploitation is not a button, it is not “epsilon“, but a real data collection strategy, which decides what the agent can learn and how good it can become.
• You will see why the training reward can lie to you, why an agent without exploration can look “better” on the graph, but actually be weaker in reality.
• You will learn where exploration actually occurs in an Markov Decision Process(MDP), not only in actions, but also in states and in the agent’s policy; and why this matters enormously.
• You will understand what exploiting a wrong policy means, how lock-in occurs, why exploiting too early can destroy learning, and what this looks like in practice.
• You will learn the different types of exploration in modern RL: epsilon, entropy, optimism, uncertainty, curiosity; and what each solves and where it falls short.
• You will learn to interpret data correctly: when reward means something, when it doesn’t, what entropy means, action diversity, state distribution and seed sensitivity.
• You will see everything in practice, in a FrozenLake + DQN case study, with three types of exploration: no exploration, large exploration and controlled exploration; and you will understand what is really happening and why.

Link: Exploration vs Exploitation in Reinforcement Learning