DQN Breakout
Skills:
RL Foundations85%
This video illustrates the improvement in the performance of DQN over training (i.e. after 100, 200, 400 and 600 episodes). After 600 episodes DQN finds and exploits the optimal strategy in this game, which is to make a tunnel around the side, and then allow the ball to hit blocks by bouncing behind the wall. Note: the score is displayed at the top left of the screen (maximum for clearing one screen is 448 points), number of lives remaining is shown in the middle (starting with 5 lives), and the “1” on the top right indicates this is a 1-player game.
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RL Course by David Silver - Lecture 8: Integrating Learning and Planning
Google DeepMind
RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning
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RL Course by David Silver - Lecture 2: Markov Decision Process
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RL Course by David Silver - Lecture 5: Model Free Control
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RL Course by David Silver - Lecture 6: Value Function Approximation
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RL Course by David Silver - Lecture 4: Model-Free Prediction
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RL Course by David Silver - Lecture 3: Planning by Dynamic Programming
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RL Course by David Silver - Lecture 10: Classic Games
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RL Course by David Silver - Lecture 7: Policy Gradient Methods
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Google DeepMind: Ground-breaking AlphaGo masters the game of Go
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Match 1 - Google DeepMind Challenge Match: Lee Sedol vs AlphaGo
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Match 2 - Google DeepMind Challenge Match: Lee Sedol vs AlphaGo
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Match 1 15 min Summary - Google DeepMind Challenge Match
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Match 3 - Google DeepMind Challenge Match: Lee Sedol vs AlphaGo
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Match 2 15 Minute Summary - Google DeepMind Challenge Match 2016
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Match 3 15 Minute Summary - Google DeepMind Challenge Match 2016
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Match 4 - Google DeepMind Challenge Match: Lee Sedol vs AlphaGo
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Match 4 15 Minute Summary - Google DeepMind Challenge Match 2016
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Match 5 - Google DeepMind Challenge Match: Lee Sedol vs AlphaGo
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Match 5 15 Minute Summary - Google DeepMind Challenge Match 2016
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DQN SPACE INVADERS
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DQN Breakout
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Asynchronous Methods for Deep Reinforcement Learning: Labyrinth
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Asynchronous Methods for Deep Reinforcement Learning: MuJoCo
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Asynchronous Methods for Deep Reinforcement Learning: TORCS
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Differentiable neural computer family tree inference task
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StarCraft II DeepMind feature layer API
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DeepMind Health – Partnership with the Royal Free London NHS Foundation Trust
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DeepMind Health – Michael Wise – a patient's journey
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Streams – a platform for a digital NHS
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DeepMind Lab - Nav Maze Level 1
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DeepMind Lab - Stairway to Melon Level
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DeepMind Lab - Laser Tag Space Bounce Level (Hard)
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Exploring the mysteries of Go with AlphaGo and China's top players
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Demis Hassabis on AlphaGo: its legacy and the 'Future of Go Summit' in Wuzhen, China
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The Future of Go Summit: AlphaGo & Ke Jie match 1 moves analysis
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The Future of Go Summit: AlphaGo & Ke Jie match 2 moves analysis
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The Future of Go Summit: Pair Go moves analysis
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The Future of Go Summit: AlphaGo & Ke Jie match 3 moves analysis
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Emergence of Locomotion Behaviours in Rich Environments
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StarCraft II 'mini games' for AI research
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Trained and untrained agents play StarCraft II full 1vs1 game
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DeepMind open source PySC2 toolset for Starcraft II
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ICML 2017: Test of Time Award (Sylvain Gelly & David Silver)
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Ke Jie and DeepMind's Go Ambassador Fan Hui review the 3rd AlphaGo vs Ke Jie game
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Ke Jie and DeepMind's Go Ambassador Fan Hui review the 1st AlphaGo vs Ke Jie game
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Ke Jie and DeepMind's Go Ambassador Fan Hui review the 2nd AlphaGo vs Ke Jie game
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AlphaGo Zero: Discovering new knowledge
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AlphaGo Zero: Starting from scratch
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Defining principles for tech companies in the NHS: DeepMind Health's Collaborative Listening Summit
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A systems neuroscience approach to building AGI - Demis Hassabis, Singularity Summit 2010
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Retour de Rémi Munos en France et ouverture de DeepMind Paris
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Grid cells - Caswell Barry, UCL
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DeepMind Health Research and Moorfields Eye Hospital NHS Foundation Trust: What our research shows
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DeepMind Health Research and Moorfields Eye Hospital NHS Foundation Trust: A Patient's Story
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Deep Learning 3: Neural Networks Foundations
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Deep Learning 5: Optimization for Machine Learning
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Deep Learning 8: Unsupervised learning and generative models
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Reinforcement Learning 1: Introduction to Reinforcement Learning
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Deep Learning 2: Introduction to TensorFlow
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