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Reinforcement Learning

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Yigit UNALLAR
Yigit UNALLAR

Discovering learning by interaction on agent environment

Ingenieurwesen
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Reinforcement Learning Yigit UNALLAR
Machine Learning Learn without explicitly programmed! ● Supervised Learning ● Unsupervised Learning ● Reinforcement Learning
Reinforcement Learning ● Learning from interaction! ○ Driving a car, ○ Holding a conversation, ● Goal-directed approach ○ Closed-loop, ○ Reward oriented,
Reinforcement vs. Unsupervised Learning ● Hidden structures! ● Unlabeled data! ● No reliance on structures! ● Maximize a reward!
Exploration vs. Exploitation Dilemma ● Exploit to obtain rewards! ● Explore to perform better! ● Either Exploration or Exploitation? ● Closest to the human and animal learning!
Examples ● Mobile Robot ○ More trash to find, ○ Way back to battery station, ● Adaptive Controller for Petrol Refinery ○ Optimize yield/cost/quality, ○ Specified marginal costs,
Agent & Environment ● Policy, ○ Mapping from states to actions, ● Reward, ○ Pain, pleasure, ● Value Function, ○ Farsighted judgement, ● Model, ○ Mimics the environment,
Pick and Place Robot Action: Voltages at motors, States: Latest joint data, Reward: +1 for successful pick-up, computed in the environment!
Goals & Markov Decision Process Goals: Markov Decision Process: Retaining all relevant information, Markov Property!
Markov Decision Process ctd. MDP if, ● The state and action spaces are finite, ● Satisfies Markov property, Example: Recycling Robot ● Actively search for a can, ● Remain still and wait for a can, ● Go back to station,
Recycling Robot
Value Functions- Bellman Equations Solving RL tasks for WHAT?! ● Finding a policy ○ Achieves lots of reward ■ Over the long RUN!
Recycling Robot Revised
Dynamic Programming ● Use value functions, ● Organize and structure a search, ● GOOD POLICIES!
Dynamic Programming
Monte Carlo Methods ● Used in algorithm to mimic policy iteration, ○ Policy Evaluation, ■ (s,a) averages over time ==> Q ○ Policy Iteration, ■ Next policy from Q, (Greedy Policy), ● Given s, new policy returns a that max Q(s, . ) ● Works in episodic problems ONLY!
Any Questions?
References [1] Reinforcement Learning: Introduction, R. Sutton, A. Barto [2] AIMA, S. Russell, P. Norvig

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Reinforcement Learning