PPT-Graph Neural Net work and Reinforcement Learning in EDA and beyond

Callie Hao Assistant Professor ECE Georgia Institute of Technology Sharclab Georgia Tech httpssharclabecegatechedu Background Graph Neural Network GNN Reinforcement Learning RL. Anderson Douglas C Hittle Alon D Katz and R Matt Kretchmar Department of Computer Science Colorado State University Fort Collins CO 80523 andersonkretchma cscolostateedu Department of Mechanical Engineering Colorado State University Fort Collins This paper introduces NFQ an algorithm for e64259cient and ef fective training of a Qvalue function represented by a multilayer percep tron Based on the principle of storing and reusing transition experiences a modelfree neural network based Reinfor Anderson Douglas C Hittle Alon D Katz and R Matt Kretchmar Department of Computer Science Colorado State University Fort Collins CO 80523 andersonkretchma cscolostateedu Department of Mechanical Engineering Colorado State University Fort Collins University of Wisconsin – Madison. HAMLET 2009. Reinforcement Learning. Reinforcement learning. What is it and why is it important in machine learning?. What machine learning algorithms exist for it?. 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.. Aaron Schumacher. Data Science DC. 2017-11-14. Aaron Schumacher. planspace.org has these slides. Plan. applications. : . what. t. heory. applications. : . how. onward. a. pplications: what. Backgammon. optimisation. Milica. Ga. š. i. ć. Dialogue Systems Group. Structure of spoken . dialogue systems. Language understanding. Language generation. semantics. a. ctions. 2. Speech recognition. Dialogue management. Kretov. Maksim. 5. vision. 1 November 2015. Plan. Part A: Reminders. Key definitions of RL and MDP. Bellman equations. General structure of RL . tasks. Part B: Application to Atari . games. Q-learning. Learning What is learning? What are the types of learning? Why aren’t robots using neural networks all the time? They are like the brain, right? Where does learning go in our operational architecture? Few-Shot Learning with Graph Neural Networks CS 330 Paper Presentation Problem Image source: Ravi, Sachin, and Hugo Larochelle. “Optimization as a model for few-shot learning,” 2017, 11. Some approaches to few-shot learning: 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”. Session 5: Reinforcement Learning Kenji Doya Okinawa Institute of Science and Technology Title Reinforcement learning: computational theory and neural mechanisms Abstract Reinforcement learning is a networks deep recurrent and dynamical to perform a variety of tasks using evolutionary and reinforcement learning algorithms Analyzed optimized networks using statistical and information theoretic too The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand