Deep Learning and Reinforcement Learning Summer School 2017 - Mila
Mila > Courses > Deep Learning and Reinforcement Learning Summer School 2017 Deep Learning and Reinforcement Learning Summer School 2017 DLSS: June 26th - July 1st, 2017 & RLSS: July 3rd - 5th, 2017 Both the DLSS and the RLSS are supported by the Canadian Institute For Advanced Research (CIFAR) and the Centre de Recherches Mathématiques. Video lectures available here Deep Learning Summer School: D
Deep Reinforcement Learning (John Schulman, OpenAI)
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented d
Learning Reinforcement Learning
Github Repo with code and exercises Why Study Reinforcement Learning #Reinforcement Learning is one of the fields I’m most excited about. Over the past few years amazing results like learning to play Atari Games from raw pixels and Mastering the Game of Go have gotten a lot of attention, but RL is also used in Robotics, Image Processing and Natural Language Processing. Combining Reinforcement Lear
Reinforcement Learning for Torch: Introducing torch-twrl
Open source Reinforcement Learning for Torch: Introducing torch-twrl Advances in machine learning have been driven by innovations and ideas from many fields. Inspired by the way that humans learn, Reinforcement Learning (RL) is concerned with algorithms which improve with trial-and-error feedback to optimize future performance. Board games and video games often have well-defined reward functions w
Deep Reinforcement Learning: Pong from Pixels
This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of t
Gym
Gym is a standard API for reinforcement learning, and a diverse collection of reference environments# The Gym interface is simple, pythonic, and capable of representing general RL problems: import gym env = gym.make("LunarLander-v2", render_mode="human") observation, info = env.reset(seed=42) for _ in range(1000): action = policy(observation) # User-defined policy function observation, reward, ter
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Tech-Circle #18 Pythonではじめる強化学習 OpenAI Gym 体験ハンズオン
Sutton & Barto Book: Reinforcement Learning: An Introduction
The Effects of Feedback on Human Behavior in Social Media: An Inverse Reinforcement Learning Model Sanmay Das Washington University in St. Louis sanmay@seas.wustl.edu Allen Lavoie Washington University in St. Louis allenlavoie@wustl.edu ABSTRACT We intro