PPT-Double Dueling Agent for Dialogue Policy Learning

YuAn Wang httpsgithubcomMiuLabE2EDialog Double Dueling DQN Microsoft Dialogue Challenge Outline Variants of DQN DQN Double DQN Dueling DQN Prioritized DQN Distributional DQN Exploration Strategies. Lecture 11: Reinforcement Learning. Jiang Bian, Fall 2012. University of Arkansas at Little Rock. Reinforcement Learning. In MDP, we learned how to determine an . optimal sequence of actions for an agent in a stochastic environment. Reinforcement Learning . &. Designing Underactuated Robotic . Hands. Jasper Haag, Young Scholar, Malden High School. Alex Cohen, Young Scholar, Needham High School. Purpose. -Develop Blackjack policies for a computer to use. COSC 878 Doctoral Seminar. Georgetown University. Presenters:. . Tavish Vaidya, . Yuankai Zhang. Jan 20, 2014. When an infant plays, waves its arms, or looks about, it has no explicit teacher, but it does have a direct sensorimotor connection to its environment. Conference Paper July 2010. ESCalate. -. sponsored Project. RMHPilkington@uclan.ac.uk. Aims. To explore how . dialogue can be used:. To assess knowledge. To assess practice. To evidence reflection on . POMDP-based Dialogue Managers. M. Gašić. , . F. Jurčíček, S. Keizer, F. Mairesse, B. Thomson, K. Yu, S. Young. Cambridge University Engineering Department | {mg436, . fj228. , sk561, farm2, brmt2, ky219, sjy}@eng.cam.ac.uk. Milica. Ga. š. i. ć. Dialogue Systems Group. What constitutes a spoken dialogue system?. . Understanding . the user. Deciding what . to say back. Conducting a . conversation beyond simple . voice commands . Write-up. Generally, 1-2 pages. What was your idea? . How did you try to accomplish it? . What worked? . What you could have done differently?. Video. Show off your idea working. Target audience: Junior/Senior EECS students who haven’t taken this class. Overview. Introduction. Q-learning. Exploration Exploitation. Evaluating RL algorithms. On-Policy learning: SARSA. Model-based Q-learning. What Does Q-Learning learn. Does Q-learning gives the agent an optimal policy? . 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. Proposal preparation Capacity Building workshop. 11 September 2018. SA-EU Dialogue Facility. Welcome and Introductions. Ms Flora.  Bertizzolo:. Attache. , Cooperation . Delegation of the EU to South Africa (EUD) . DEVELOPING . INNOVATIVE LITERATURE REVIEWS. Presenter: Marilyn K. Simon, Ph.D. Co-Author: Jim Goes, Ph.D.. Li. Literature Review (LR). Historically. , formal syntheses of research can be traced to the 17th and 18th . Overview. Introduction to Reinforcement Learning. Finite Markov Decision Processes. Temporal-Difference Learning (SARSA, Q-learning, Deep Q-Networks) . Policy Gradient Methods (Finite . D. ifference Policy Gradient, REINFORCE, Actor-Critic). 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”. Large dataset of isolated, labeled images. Where do the images come from?. What do we do with the labels?. , 1), . , 1), . , 0) , . …. }. {(. (. (. Embodied agents. Agents that perceive and act in the world.