Interactive Robot Education Riad Akrour, Marc Schoenauer, Mich` ele - PowerPoint PPT Presentation

Dec 28, 2023 •10 likes •540 views

Interactive Robot Education Riad Akrour, Marc Schoenauer, Mich` ele Sebag RL-Feedbacks @ECMLPKDD, Praha, sept. 2013 Swarm Robotics Swarm-bot (2001-2005) Swarm Foraging, UWE Symbrion IP, 2008-2013; http://symbrion.org/ This talk: Train a

Interactive Robot Education Riad Akrour, Marc Schoenauer, Mich` ele Sebag RL-Feedbacks @ECMLPKDD, Praha, sept. 2013
Swarm Robotics Swarm-bot (2001-2005) Swarm Foraging, UWE Symbrion IP, 2008-2013; http://symbrion.org/
This talk: Train a resource-bounded robot
Reinforcement Learning, formal background Notations ◮ State space S ◮ Action space A ◮ Transition p ( s , a , s ′ ) �→ [0 , 1] ◮ Reward r ( s ) ◮ Discount 0 < γ < 1 Goal: a policy π mapping states onto actions π : S �→ A s.t. Maximize E [ π | s 0 ] = Expected discounted cumulative reward t γ t +1 p ( s t , a = π ( s t ) , s t +1 ) r ( s t +1 ) = r ( s 0 ) + �
Robot: innate vs acquired knowledge What is designed, what is learned ? ◮ States, actions are designed and provided ◮ Rewards are designed ◮ Transition model: provided or learned The sought output: a policy π mapping states onto actions with maximal expected cumulative reward � T � � J ( π ) = I E r t | π t =1 where π �→ trajectory: ( s 0 , a 0 , r 0 , s 1 , s 1 , r 1 , . . . s T ) Key feature: a data-intensive approach.
What does Reinforcement Learning need ? ◮ A reward function standard RL Sutton-Barto 08; Szepesv´ ari 10 ◮ An expert demonstrating an “optimal“ behavior inverse RL Abbeel 04-12; Billard et al. 05-13 ◮ A knowledgeable teacher preference-based RL Akrour et al. 11-12; Cheng et al 11; Wilson et al. 12 ◮ A knowledgeable and moderately reliable teacher this talk
Find the treasure Single reward: on the treasure. ��
Wandering robot Nothing happens... ��
The robot finds it ��

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