PDF-Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu David

gravesioannisdaanmartinriedmiller deepmindcom Abstract We present the 64257rst deep learning model to successfully learn control policies di rectly from highdimensional sensory input using reinforcement learning The model is a convolutional neural n. The deterministic pol icy gradient has a particularly appealing form it is the expected gradient of the actionvalue func tion This simple form means that the deter ministic policy gradient can be estimated much more ef64257ciently than the usual sto nyuedu mmathieuclipperensfr Abstract We propose an unsupervised method for learning multistage hierarchies of sparse convolutional features While sparse coding has become an in creasingly popular method for learning visual features it is most often t and Charlotte Radiology Belk Mobile Mammography Center Silver COMMUNITY RELATIONS Government United States Holocaust Memorial Museum Edelman US Holocaust Memorial Museum 20th Anniversary National Tour Tribute AOE COMMUNITY RELATIONS Government New com Abstract Applying convolutional neural networks to large images is computationally ex pensive because the amount of computation scales linearly with the number of image pixels We present a novel recurrent neural network model that is ca pable of com Koray Kavukcuoglu DeepMind Technologies koraydeepmindcom Abstract Continuousvalued word embeddings learned by neural language models have re cently been shown to capture semantic and syntactic information about words very well setting performance Atari’s Second System. Joe Decuir, Atari Alumnus. Standards Architect, CSR. Distinguished Lecturer, . IEEE Consumer Electronics . Society. Jdecuir@ieee.org. Agenda. Lessons Atari learned from the Atari VCS (aka 2600). Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . “Back of the Napkin”. Wayne Zage, Center Director. ●. “My First IAB Meeting with Alex”. Don Price, NSF Program Manager. ●. “A Detail Man”. Craig Scott, Assessment Coordinator. ●. “Alex, My Mentor”. CS 4730 – Computer Game Design. 2. My Gaming CV. Game Systems Owned. Early: C-64, . Intellivision. Nintendo: NES, SNES, N64, . Gamecube. , Wii, Game Boy, GB Color, GBA, GBA SP, GB Micro, DS, DS Lite, 3DS XL. 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.. Act II Study Guide. When does Act II begin? How long have the families been in hiding?. January 1944. (July 1942 – January 1944). 2. . What does the following passage tell the reader about Anne Frank?. Deep Reinforcement Learning Sanket Lokegaonkar Advanced Computer Vision (ECE 6554) Outline The Why? Gliding Over All : An Introduction Classical RL DQN-Era Playing Atari with Deep Reinforcement Learning [2013] The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand