An epsilon greedy policy is a reinforcement learning strategy that chooses a random action with probability epsilon and otherwise chooses the action currently estimated to be best. The method supports exploration early in training and can gradually shift toward exploitation as epsilon is reduced.
In reinforcement learning, a policy that either follows a random policy with epsilon probability or a greedy policy otherwise. For example, if epsilon is 0.9, then the policy follows a random policy 90% of the time and a greedy policy 10% of the time. Over successive episodes, the algorithm reduces epsilon's value in order to shift from following a random policy to following a greedy policy. By shifting the policy, the agent first randomly explores the environment and then greedily exploits the results of random exploration.