PPT-CSCE-689 Reinforcement Learning

Author : CitySlicker | Published Date : 2022-08-01

Deep Qlearning Instructor Guni Sharon 1 CSCE689 Reinforcement Learning Stateless decision process Markov decision process Solving MDPs offline Dynamic programming

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CSCE-689 Reinforcement Learning: Transcript

Deep Qlearning Instructor Guni Sharon 1 CSCE689 Reinforcement Learning Stateless decision process Markov decision process Solving MDPs offline Dynamic programming MonteCarlo Temporal difference. Jared Christen. Tetris. Markov decision processes. Large state space. Long-term strategy without long-term knowledge. Background. Hand-coded algorithms can clear > 1,000,000 lines. Genetic algorithm by Roger . www.reising.comphone 248-689-3500 fax 248-689-4071 The Obvious Advantageby Eric T. JonesAlmost all inventions are combinations of existing technology. However, the patent laws of virtually every nati Hector Munoz-Avila. Stephen Lee-Urban. www.cse.lehigh.edu/~munoz/InSyTe. Outline. Introduction. Adaptive Game AI. Domination games in Unreal Tournament©. Reinforcement Learning. Adaptive Game AI with Reinforcement Learning. Scripting Languages and Rapid Prototyping. What are Scripting Languages?. tr.v. . script·ed, script·ing, . scripts. To . prepare (a text) for filming or . broadcasting.. To . orchestrate. (behavior or an event, for example) as if writing a script: . Goal .  How do we learn behaviors through . classical conditioning. ?. Learning is…. Relatively permanent. Change in behavior. Due to experience. Behaviorism. .  Psychology . should focus on observable . History. Established by Joseph . Klapper. (1960). Released a book ‘The Effects of Mass Communication’. Suggested that the media has little power to influence people. Thought it was important to move away from thinking that the media is all powerful in influence. Human-level control through deep . reinforcment. learning. Dueling Network Architectures for Deep Reinforcement Learning. Reinforcement Learning. Reinforcement learning is a computational approach to understanding and automating good directed learning and decision making. It learns by interacting with the environment.. Differential Schedules. Also called . Differentiation or IRT . schedules. .. Usually used with reinforcement . Used where the reinforcer depends BOTH on time and . the . number of reinforcers.. Provides . Equal Pay Cases. Case 1: A tenured female associate professor in the industrial technology department is employed at a salary lower than male colleagues who are the same rank and teach similar courses at the same location. She is the second-lowest-paid professor in a department of close to 20, despite the fact that she has a higher rank and more seniority than four male colleagues. Does the scenario violate the Equal Pay Act?. CSCE 190: Computing in the Modern World. Jason D. Bakos. Heterogeneous and Reconfigurable Computing Group. Apollo 11 vs iPhone 10. Apollo 11. Cost: ~$200 billion. (whole program, adjusted). Guidance Computer (1966):. Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”. - 1 - Cityordinanceviolationswhicharetheequivalentstatestatuteviolationslistedthisscheduleshallwaiverableandcarrythepenaltylistedunlesstheschedulefineexceedsthemaximumallowedby cityordinance.Cityordin !"#$%&'()*+,-)&.(/#),&0"%,(/#1&2+()%(/"%*&(3&%,*&4#(1*)*&(3&0"56*$&&!"#$%&'()*"#&+%&',-"$*"#)*'./&0*01"'23405) \n\n\r \n\n\r -/0/12/3413056-/00123405nnrnnrrnrnnrnrnn rnrnrrnnrnrr-nrnrn/n0 nr0 rrr0n/0r1nnr-n0r2nnn0-nrnnr0r//nn3rnr0/nnrn/00-/0110234561572268229572262255226822955657226822768670x0000r0rnrnrrnrrrnrnrrnrrrrnrrrnr

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