Reinforcement Learning

Learn action selection for probabilistic applications

• Robot learning to dock on battery charger
• Learning to choose actions to optimize factory output
• Learning to play Backgammon

Note several problem characteristics:

• Delayed reward
• Opportunity for active exploration
• Possibility that state only partially observable
• Possible need to learn multiple tasks with same sensors/effectors