HackerRL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning
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Further reading 2
Where does learning get a signal without an answer key? →Start with an unlabeled patch of an image, then switch to a game that requires a sequence of actions. Neither has an answer key, but finding structure and choosing a move need different signals.This video helps answer · Where does the learning signal come from?What improved when a game agent got better? →A rising score might reflect a better network, deeper search or a loophole. Trace what the agent observes, what it tries and why the resulting move earns a reward.This video helps answer · Reward and fitness both give feedback. What changes?
Caption notes
Reinforcement learning concerns actions whose consequences affect later observations and rewards. The lecture distinguishes policy, value, model, planning and exploration.
Original passages · 6
the agent is actually influencing the data that it seesthe future is independent of the past given the presentin RL the environment is unknown in planning the environment is knownexploration means choosing to give up some reward that you know about in order to find out more about the environmentthere's no supervisor there's just this reward signalthe state is a sufficient statistic of the future