Exploration is needed to find unknown actions which lead to very large rewards. Most of the reinforcement learning algorithms share one problem: they learn by trying different actions and seeing which works better. We can use a few made-up heuristics (e.g. epsilon-greedy exploration) to mitigate the problem and speed up the learning process. Multi-armed bandits … Continue reading Exploration and Planning in Reinforcement Learning