Keras Reinforcement Learning Projects

Exploration versus exploitation

Ideally, the agent must associate with each action a_t the respective reward r, in order to then choose the most rewarding behavior for achieving the goal. This approach is therefore impracticable for complex problems in which the number of states is particularly high and, consequently, the possible associations increase exponentially.

This problem is called the exploration-exploitation dilemma. Ideally, the agent must explore all possible actions for each state, finding the one that is actually most rewarded for exploiting in achieving its goal.

Thus, decision-making involves a fundamental choice:

• Exploitation: Make the best decision, given current information
• Exploration: Collect more information

In this ...