CS 294 Deep Reinforcement Learning, Spring 2017
If you are a UC Berkeley undergraduate student looking to enroll in the fall 2017 offering of this course: We will post a form that you may fill out to provide us with some information about your background during the summer. Please do not email the instructors about enrollment: the form will be used to collect all information we need.
Adversarial Attacks on Neural Network Policies
Sandy Huang†, Nicolas Papernot‡, Ian Goodfellow§, Yan Duan†§, Pieter Abbeel†§ †University of California, Berkeley, Department of Electrical Engineering and Computer Sciences ‡Pennsylvania State University, School of Electrical Engineering and Computer Science §OpenAI Abstract — Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclas
The Nuts and Bolts of Deep RL Research
The Nuts and Bolts of Deep RL Research John Schulman December 9th, 2016 Outline Approaching New Problems Ongoing Development and Tuning General Tuning Strategies for RL Policy Gradient Strategies Q-Learning Strategies Miscellaneous Advice Approaching New Problems New Algorithm? Use Small Test Problems I Run experiments quickly I Do hyperparameter search I Interpret and visualize learning process:
2015.10.5.dagger
DAGGER and Friends John Schulman 2015/10/5 References: 1. A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning, Ross, Gordon & Bagnell (2010). DAGGER algorithm 2. Reinforcement and Imitation Learning via Interactive No-Regret Learning Ross & Bagnell (2014). AGGREVATE algorithm 3. Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search P
Deep Reinforcement Learning Workshop
The webpage for the NIPS 2016 Deep RL workshop is here. Deep Reinforcement Learning Workshop, NIPS 2015 The first-ever Deep Reinforcement Learning Workshop will be held at NIPS 2015 in Montréal, Canada on Friday December 11th. More details about the program are coming soon. Organizers: John Schulman, Pieter Abbeel, David Silver, and Satinder Singh. Contact Us Co-Sponsored by Osaro and Google DeepM
BARS 2015 | Home
BARS 2015 Venue: Chevron Auditorium, International House, 2299 Piedmont Avenue, UC Berkeley Date: Friday October 23, 2015 Time: 9:00am - 6:00pm The 2015 Bay Area Robotics Symposium aims to bring together roboticists from the Bay Area. The program will consist of a mix of faculty, student and industry presentations. Here are the schedule and the lineup of speakers. Symposium Chairs: Pieter Abbeel (
Computation Graph Toolkit — CGT 0.1 documentation
Computation Graph Toolkit¶ Computation Graph Toolkit (CGT) is a library for evaluation and differentiation of functions of multidimensional arrays. Source code is available on GitHub. What Does It Do?¶ The core features are as follows: Automatic differentiation of functions involving multidimensional arrays, using computation graphs Compile fast implementations of array computations that can be ru
Deep Sensorimotor Learning
Sensorimotor Deep Learning In this project we are developing deep learning methods for robotics. Deep learning is a branch of machine learning that is concerned with learning structure, representations, and underlying patterns in complex “raw” data, such as images and sounds. Traditionally, these methods have been applied to passive tasks, like recognizing cars or pedestrians in camera images, whe
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