DOCSLIB.ORG

DS595/CS525 Reinforcement Learning Prof

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
DS595/CS525 Reinforcement Learning Prof This lecture will be recorded!!! Welcome to DS595/CS525 Reinforcement Learning Prof. Yanhua Li Time: 6:00pm –8:50pm R Zoom Lecture Fall 2020 Quiz 3 today Week 7 (10/15 R) v Model-Free Control § 30 min at the beginning Quiz 4 next Thursday Week 8 (10/22 R) v Linear Value Function Approximation （20 mins） § Stochastic Gradient Decent § VFA for policy evaluation § VFA for control Last Lecture v Value Function Approximation VFA § Introduction § VFA for Policy Evaluation § VFA for Control This Lecture v Non-linear value function approximation § Intro of Deep Reinforcement Learning (DRL) § Review on Deep Learning § Deep Q-Learning § Project 3 (by Yingxue) • Pytorch configuration and Google cloud environment This Lecture v Non-linear value function approximation § Intro of Deep Reinforcement Learning (DRL) § Review on Deep Learning § Deep Q-Learning § Project 3 (by Yingxue) • Pytorch configuration and Google cloud environment RL algorithms Tabular Representation Function Representation Value Function Value Function v Model-based Model-Free Policy function control Control approximation § Policy § Policy evaluation (DP) evaluation Value Function MC (First/every approximation visit) and TD Linear/Non-linear § Policy iteration § Value/Policy § Value iteration Iteration (Asynchronous) • MC Iteration Advantage Actor Critic: • TD Iteration – SARSA A2C – Q-Learning A3C – Double Q- Learning Reinforcement Inverse Learning Reinforcement Learning Tabular representation of reward Linear reward function learning Model-based control Imitation learning Model-free control Apprenticeship learning (MC, SARSA, Q-Learning) Inverse reinforcement learning MaxEnt IRL Function representation of reward MaxCausalEnt IRL 1. Linear value function approx MaxRelEnt IRL (MC, SARSA, Q-Learning) 2. Value function approximation (Deep Q-Learning, Double DQN, Non-linear reward function learning Single Agent Single prioritized DQN, Dueling DQN) Generative adversarial 3. Policy function approximation imitation learning (GAIL) (Policy gradient, PPO, TRPO) 4. Actor-Critic methods Adversarial inverse reinforcement (A2C, A3C) learning (AIRL) Review of Deep Learning Review of Generative Adversarial nets As bases for non-linear function approximation (used in 2-4). Multi-Agent Reinforcement Learning Multi-Agent Inverse Reinforcement Multi-agent Actor-Critic Learning etc. MA-GAIL MA-AIRL AMA-GAIL Agents Applications Multiple Review: Value Function Approximation (VFA) v Represent a (state-action/state) value function with a parameterized function instead of a table v Vπ(s) s Vπ(s;w) w v Qπ(s,a) s Qπ(s,a;w) a w Linear Value Function Approximation (VFA) v Use features to represent both the state and action v Represent state-action value function (Q-function) v Stochastic gradient descent update: From full gradient to Stochastic gradient Linear VFA Linear VFA model-free control v Similar to model-free policy evaluation, true Q(s,a) is unknown, and so substitute a target value v In MC methods, use a return Gt as a target value Linear VFA model-free control v Similar to model-free policy evaluation, true Q(s,a) is unknown, and so substitute a target value v In MC methods, use a return Gt as a target value v For SARSA, use a target value of Linear VFA model-free control v Similar to model-free policy evaluation, true Q(s,a) is unknown, and so substitute a target value v In MC methods, use a return Gt as a target value v For SARSA, use a target value of v For Q-Learning, use a target value of Non-Linear VFA model-free control v Similar to model-free policy evaluation, true Q(s,a) is unknown, and so substitute a target value v In MC methods, use a return Gt as a target value v For SARSA, use a target value of v For Q-Learning, use a target value of Non-Linear VFA model-free control v Similar to model-free policy evaluation, true Q(s,a) is unknown, and so substitute a target value v In MC methods, use a return Gt as a target value v For SARSA, use a target value of v For Q-Learning, use a target value of Model-Free Q-Learning Control With linear/non-linear VFA Model-Free Q-Learning Control With linear VFA Model-Free Q-Learning Control with general (non-linear) VFA This Lecture v Non-linear value function approximation § Intro of Deep Reinforcement Learning (DRL) § Review on Deep Learning § Deep Q-Learning § Project 3 (by Yingxue) • Pytorch configuration and Google cloud environment Deep Learning! All the parameters of the logistic regressions are jointly learned. x z1 " “Neuron” 1 + !! z + y z2 " x2 + !# Feature Transformation Classification Neural Network Deep Learning! All the parameters of the logistic regressions are jointly learned. x z1 " “Neuron” 1 + !! z + y z2 " x2 + !# Feature TransformationSigmoid Function s (Classificationz) 1 s (z) = Neural1+ e-z Network z Deep learning attracts lots of attention. v I believe you have seen lots of exciting results before. Deep learning trends at Google. Source: SIGMOD 2016/Jeff Dean Neural Network + s (z) + s (z) + s (z) + s (z) “Neuron” Neural Network Different connection leads to different network structures Network parameter !: all the weights and biases in the “neurons” Fully Connect Feedforward Network neuron Input Layer Layer Layer Output 1 2 L y x1 …… 1 x2 …… y2 …… …… …… …… …… xN …… yM Input Output Layer Hidden Layer Layers Deep = Many hidden layers 152 layers Special structure 3.57% 7.3% 6.7% 16.4% AlexNet VGG GoogleNet Residual Net Taipei (2012) (2014) (2014) (2015) 101 Example Application Input Output 0.1y1 is 1 x1 x 2 0.7y2 is 2 The image …… …… …… is “2” 0.2y is 0 x256 10 16 x 16 = 256 Ink → 1 Each dimension represents No ink → 0 the confidence of a digit. Example Application v Handwriting Digit Recognition x1 y1 is 1 x2 y2 is 2 …… …… MachineNeural “2…… Network ” x 256 What is needed is a y10 is 0 function …… Input: output: 256-dim 10-dim vector vector Example Application Input Layer Layer Layer Output 1 2 L x1 …… y1 is 1 x 2 …… A function set containing the y2 is 2 …… …… …… …… …… candidates for “2…… Handwriting Digit Recognition ” x …… N y10 is 0 Input Output Layer Hidden Layer Layers You need to decide the network structure to let a good function in your function set. Loss for an Example target “1 ” …… #" x1 y1 ! 1 x Softmax 2 #" Given a set ……of y2 " 0 …… …… …… …… …… parameters …… Cross Entropy x 256 …… y10 #"!# 0 !& # #% " # , #% = − ( #)$"*#$ $%! Softmax (3 classes as example) Probability: n 1 > #$ > 0 n ∑$ #$ = 1 yi = P(Ci | x) Softmax 3 3 z 20 0.88 z z j e 1 1 z1 e ÷ y1 = e åe j=1 0.12 3 1 z 2.7 z z j z e 2 2 2 e ÷ y2 = e åe j=1 0.05 ≈0 3 - z3 z z z3 e e ÷ 3 j 3 y3 = e åe 3 j=1 + åez j j=1 Total Loss Total Loss: ( + = ( "' For all training data '%! … x1 NN y1 #'! (! Find a function in x2 NN y2 #'" (" function set that minimizes total loss L x3 NN y3 #'& & …… …… ( …… …… Find the network ∗ xN NN yN #'% parameters , that (% minimize total loss L Gradient Descent - Compute +,⁄+)! .+ )! 0.2 0.15 −. +,⁄+)! ./! .+ Compute +,⁄+)" ) " -0.1 0.05 2+ = ./# … … −. +,⁄+)" ⋮ .+ Compute +,⁄+/ ! .1! /! 0.3 0.2 ⁄ ⋮ … … −. +, +/! gradient Gradient Descent - Compute +,⁄+)! Compute +,⁄+)! )! 0.2 0.15 0.09 ⁄ −. +,⁄+)! −. +, +)! … Compute +,⁄+)" Compute +,⁄+)" … )" -0.1 0.05 0.15 … … −. +,⁄+) −. +,⁄+)" " … … Compute +,⁄+/! Compute +,⁄+/! /! 0.3 0.2 0.10 ⁄ −. +,⁄+/! … … −. +, +/! … … Backpropagation v Backpropagation: an efficient way to compute +,⁄+) in neural network libdnn Why Deep Learning? Deep models are better than shadow models? Take DS504 in Spring 2020 This Lecture v Non-linear value function approximation § Intro of Deep Reinforcement Learning (DRL) § Review on Deep Learning § Deep Q-Learning § Project 3 (by Yingxue) • Pytorch configuration and Google cloud environment Generalization with Deep Reinforcement Learning v Using function approximation to help scale up to making decisions in really large domains Generalization with Deep Reinforcement Learning v Use deep neural networks to represent § Value function (Deep Q-Learning, etc.) § Policy function (Policy Gradient, etc.) § Both value function and policy (A2C, A3C) v Optimize loss function by stochastic gradient descent (SGD) Recall: Non-Linear VFA model-free control v Similar to model-free policy evaluation, true Q(s,a) is unknown, and so substitute a target value v In MC methods, use a return Gt as a target value v For SARSA, use a target value of v For Q-Learning, use a target value of Recall: Model-Free Q-Learning Control with general (non-linear) VFA ??????????Going to work? https://deepmind.com/research/open-source/dqn - - - Breakout game demo https://www.youtube.com/watch?v=TmPfTpjtdgg Recall: Version 1 * * https://papers.nips.cc/paper/3964-double-q-learning.pdf - Example: TD policy evaluation s1 s2 s3 s4 s5 s6 a1 a2 Taxi passenger-seeking process: R=[1,0,0,0,3,0] For any action, π(s) = a1, ∀s, γ = 1. any action from s1 and s6 terminates episode Given (s3, a1, 0, s3, a1, 0, s2, a1, 0, s1, a1, 1, T); Q1: First visit MC estimate of V of each state? [111000] Q2: TD estimate of states (init at 0) with α=1? [100000] Q3: Now get to chose 2 ”replay” backups to do. Which should we pick to get best estimate? Example: TD policy evaluation s1 s2 s3 s4 s5 s6 a1 a2 Taxi passenger-seeking process: R=[1,0,0,0,3,0] For any action, π(s) = a1, ∀s, γ = 1. any action from s1 and s6 terminates episode Given (s3, a1, 0, s3, a1, 0, s2, a1, 0, s1, a1, 1, T); Q1: First visit MC estimate of V of each state? [111000] Q2: TD estimate of states (init at 0) with α=1? [100000] Q3: Now get to chose 2 ”replay” backups to do.
Load more

Recommended publications
  • A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
    Journal of Machine Learning Research 22 (2021) 1-82 Submitted 9/19; Revised 8/20; Published 1/21 A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms Oliver Kroemer∗ [email protected] School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA Scott Niekum [email protected] Department of Computer Science The University of Texas at Austin Austin, TX 78712, USA George Konidaris [email protected] Department of Computer Science Brown University Providence, RI 02912, USA Editor: Daniel Lee Abstract A key challenge in intelligent robotics is creating robots that are capable of directly in- teracting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges. Keywords: manipulation, learning, review, robots, MDPs 1. Introduction Robot manipulation is central to achieving the promise of robotics|the very definition of a robot requires that it has actuators, which it can use to effect change on the world.
    [Show full text]
  • Self-Corrective Apprenticeship Learning for Quadrotor Control
    Self-corrective Apprenticeship Learning for Quadrotor Control Haoran Yuan Self-corrective Apprenticeship Learning for Quadrotor Control by Haoran Yuan to obtain the degree of Master of Science at the Delft University of Technology, to be defended publicly on Wednesday December 11, 2019 at 10:00 AM. Student number: 4710118 Project duration: December 12, 2018 – December 11, 2019 Thesis committee: Dr. ir. E. van Kampen, C&S, Daily supervisor Dr. Q. P.Chu, C&S, Chairman of committee Dr. ir. D. Dirkx, A&SM, External examiner B. Sun, MSc, C&S An electronic version of this thesis is available at http://repository.tudelft.nl/. Contents 1 Introduction 1 I Scientific Paper 5 II Literature Review & Preliminary Analysis 23 2 Literature Review 25 2.1 Reinforcement Learning Preliminaries . 25 2.2 Temporal-Difference Learning and Q-Learning . 26 2.2.1 Deep Q Learning . 27 2.3 Policy Gradient Method . 28 2.3.1 Actor-critic and Deep Deterministic Policy Gradient . 30 2.4 Reinforcement Learning for flight Control . 30 2.5 Learning from Demonstrations Preliminaries . 33 2.6 Learning from Demonstrations: Mapping . 34 2.6.1 Mapping Methods without Reinforcement Learning . 35 2.6.2 Mapping Methods with Reinforcement Learning . 35 2.7 Learning from Demonstrations: Planning . 37 2.8 Learning from Demonstrations: Model-Learning . 38 2.8.1 Inverse Reinforcement Learning . 39 2.8.2 Apprenticeship Learning through Inverse Reinforcement Learning . 40 2.8.3 Maximum Entropy Inverse Reinforcement Learning . 42 2.8.4 Relative Entropy Inverse Reinforcement Learning . 42 2.9 Conclusion of Literature Review . 43 3 Preliminary Analysis 45 3.1 Simulation Setup .
    [Show full text]
  • Arxiv:1907.03146V3 [Cs.RO] 6 Nov 2020
    A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms Oliver Kroemer* [email protected] School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA Scott Niekum* [email protected] Department of Computer Science The University of Texas at Austin Austin, TX 78712, USA George Konidaris [email protected] Department of Computer Science Brown University Providence, RI 02912, USA Abstract A key challenge in intelligent robotics is creating robots that are capable of directly in- teracting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges. Keywords: Manipulation, Learning, Review, Robots, MDPs 1. Introduction arXiv:1907.03146v3 [cs.RO] 6 Nov 2020 Robot manipulation is central to achieving the promise of robotics|the very definition of a robot requires that it has actuators, which it can use to effect change on the world.
    [Show full text]
  • Deep Q-Learning from Demonstrations
    The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) Deep Q-Learning from Demonstrations Todd Hester Matej Vecerik Olivier Pietquin Marc Lanctot Google DeepMind Google DeepMind Google DeepMind Google DeepMind [email protected] [email protected] [email protected] [email protected] Tom Schaul Bilal Piot Dan Horgan John Quan Google DeepMind Google DeepMind Google DeepMind Google DeepMind [email protected] [email protected] [email protected] [email protected] Andrew Sendonaris Ian Osband Gabriel Dulac-Arnold John Agapiou Google DeepMind Google DeepMind Google DeepMind Google DeepMind [email protected] [email protected] [email protected] [email protected] Joel Z. Leibo Audrunas Gruslys Google DeepMind Google DeepMind [email protected] [email protected] Abstract clude deep model-free Q-learning for general Atari game- playing (Mnih et al. 2015), end-to-end policy search for con- Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. trol of robot motors (Levine et al. 2016), model predictive However, these algorithms typically require a huge amount of control with embeddings (Watter et al. 2015), and strategic data before they reach reasonable performance. In fact, their policies that combined with search led to defeating a top hu- performance during learning can be extremely poor. This may man expert at the game of Go (Silver et al. 2016). An im- be acceptable for a simulator, but it severely limits the appli- portant part of the success of these approaches has been to cability of deep RL to many real-world tasks, where the agent leverage the recent contributions to scalability and perfor- must learn in the real environment.
    [Show full text]
  • Third-Person Imitation Learning
    Published as a conference paper at ICLR 2017 THIRD-PERSON IMITATION LEARNING Bradly C. Stadie1;2, Pieter Abbeel1;3, Ilya Sutskever1 1 OpenAI 2 UC Berkeley, Department of Statistics 3 UC Berkeley, Departments of EECS and ICSI fbstadie, pieter, ilyasu,[email protected] ABSTRACT Reinforcement learning (RL) makes it possible to train agents capable of achiev- ing sophisticated goals in complex and uncertain environments. A key difficulty in reinforcement learning is specifying a reward function for the agent to optimize. Traditionally, imitation learning in RL has been used to overcome this problem. Unfortunately, hitherto imitation learning methods tend to require that demonstra- tions are supplied in the first-person: the agent is provided with a sequence of states and a specification of the actions that it should have taken. While powerful, this kind of imitation learning is limited by the relatively hard problem of collect- ing first-person demonstrations. Humans address this problem by learning from third-person demonstrations: they observe other humans perform tasks, infer the task, and accomplish the same task themselves. In this paper, we present a method for unsupervised third-person imitation learn- ing. Here third-person refers to training an agent to correctly achieve a simple goal in a simple environment when it is provided a demonstration of a teacher achieving the same goal but from a different viewpoint; and unsupervised refers to the fact that the agent receives only these third-person demonstrations, and is not provided a correspondence between teacher states and student states. Our methods primary insight is that recent advances from domain confusion can be utilized to yield domain agnostic features which are crucial during the training process.
    [Show full text]
  • Teaching Agents with Deep Apprenticeship Learning
    Rochester Institute of Technology RIT Scholar Works Theses 6-2017 Teaching Agents with Deep Apprenticeship Learning Amar Bhatt [email protected] Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Bhatt, Amar, "Teaching Agents with Deep Apprenticeship Learning" (2017). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Teaching Agents with Deep Apprenticeship Learning by Amar Bhatt A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Engineering Supervised by: Assistant Professor Dr. Raymond Ptucha Department of Computer Engineering Kate Gleason College of Engineering Rochester Institute of Technology Rochester, New York June 2017 Approved by: Dr. Raymond Ptucha, Assistant Professor Thesis Advisor, Department of Computer Engineering Dr. Ferat Sahin, Professor Committee Member, Department of Electrical Engineering Dr. Iris Asllani, Assistant Professor Committee Member, Department of Biomedical Engineering Dr. Christopher Kanan, Assistant Professor Committee Member, Department of Imaging Science Louis Beato, Lecturer Committee Member, Department of Computer Engineering Thesis Release Permission Form Rochester Institute of Technology Kate Gleason College of Engineering Title: Teaching Agents with Deep Apprenticeship Learning I, Amar Bhatt, hereby grant permission to the Wallace Memorial Library to reproduce my thesis in whole or part. Amar Bhatt Date iii Dedication This thesis is dedicated to all those who have taught and guided me throughout my years... My loving parents who continue to support me in my academic and personal endeavors.
    [Show full text]
  • Learning Motor Skills: from Algorithms to Robot Experiments
    Learning Motor Skills: From Algorithms to Robot Experiments Erlernen Motorischer Fähigkeiten: Von Algorithmen zu Roboter-Experimenten Zur Erlangung des akademischen Grades Doktor-Ingenieur (Dr.-Ing.) vorgelegte Dissertation von Dipl.-Ing. Jens Kober aus Künzelsau März2012—Darmstadt—D17 Learning Motor Skills: From Algorithms to Robot Experiments Erlernen Motorischer Fähigkeiten: Von Algorithmen zu Roboter-Experimenten Vorgelegte Dissertation von Dipl.-Ing. Jens Kober aus Künzelsau 1. Gutachten: Prof. Dr. Jan Peters 2. Gutachten: Prof. Dr. Stefan Schaal Tag der Einreichung: Darmstadt—D17 Bitte zitieren Sie dieses Dokument als: URN: urn:nbn:de:tuda-tuprints-12345 URL: http://tuprints.ulb.tu-darmstadt.de/12345 Dieses Dokument wird bereitgestellt von tuprints, E-Publishing-Service der TU Darmstadt http://tuprints.ulb.tu-darmstadt.de [email protected] Die Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung – Keine kommerzielle Nutzung – Keine Bearbeitung 2.0 Deutschland http://creativecommons.org/licenses/by-nc-nd/2.0/de/ Erklärung zur Dissertation Hiermit versichere ich, die vorliegende Dissertation ohne Hilfe Dritter nur mit den an- gegebenen Quellen und Hilfsmitteln angefertigt zu haben. Alle Stellen, die aus Quellen entnommen wurden, sind als solche kenntlich gemacht. Diese Arbeit hat in gleicher oder ähnlicher Form noch keiner Prüfungsbehörde vorgelegen. Darmstadt, den 13. März 2012 (J. Kober) i Abstract Ever since the word “robot” was introduced to the English language by Karel Capek’sˇ play “Rossum’s Universal Robots” in 1921, robots have been expected to become part of our daily lives. In recent years, robots such as autonomous vacuum cleaners, lawn mowers, and window cleaners, as well as a huge number of toys have been made commercially available.
    [Show full text]
  • Exploring Applications of Deep Reinforcement Learning for Real-World Autonomous Driving Systems
    Exploring Applications of Deep Reinforcement Learning for Real-world Autonomous Driving Systems Victor Talpaert1;2, Ibrahim Sobh3, B. Ravi Kiran2, Patrick Mannion4, Senthil Yogamani5, Ahmad El-Sallab3 and Patrick Perez6 1U2IS, ENSTA ParisTech, Palaiseau, France 2AKKA Technologies, Guyancourt, France 3Valeo Egypt, Cairo 4Galway-Mayo Institute of Technology, Ireland 5Valeo Vision Systems, Ireland 6Valeo.ai, France Keywords: Autonomous Driving, Deep Reinforcement Learning, Visual Perception. Abstract: Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achie- vements such as Deepmind’s AlphaGo. It has been successfully deployed in commercial vehicles like Mo- bileye’s path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthetic car simulator environments such as TORCS and CARLA. In general, DRL is still at its infancy in terms of usability in real-world applications. Our goal in this paper is to encourage real-world deployment of DRL in various autonomous driving (AD) applications. We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD sys- tems. We then discuss the challenges which must be addressed to enable further progress towards real-world deployment. 1 INTRODUCTION cific control task. The rest of the paper is structured as follows. Section 2 provides background on the vari- Autonomous driving (AD) is a challenging applica- ous modules of AD, an overview of reinforcement le- tion domain for machine learning (ML). Since the arning and a summary of applications of DRL to AD. task of driving “well” is already open to subjective Section 3 discusses challenges and open problems in definitions, it is not easy to specify the correct be- applying DRL to AD.
    [Show full text]
  • One-Shot Imitation Learning
    One-Shot Imitation Learning Yan Duanyx, Marcin Andrychowiczz, Bradly Stadieyz, Jonathan Hoyx, Jonas Schneiderz, Ilya Sutskeverz, Pieter Abbeelyx, Wojciech Zarembaz yBerkeley AI Research Lab, zOpenAI xWork done while at OpenAI {rockyduan, jonathanho, pabbeel}@eecs.berkeley.edu {marcin, bstadie, jonas, ilyasu, woj}@openai.com Abstract Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning. Specifically, we consider the setting where there is a very large (maybe infinite) set of tasks, and each task has many instantiations. For example, a task could be to stack all blocks on a table into a single tower, another task could be to place all blocks on a table into two-block towers, etc. In each case, different instances of the task would consist of different sets of blocks with different initial states. At training time, our algorithm is presented with pairs of demonstrations for a subset of all tasks. A neural net is trained such that when it takes as input the first demonstration demonstration and a state sampled from the second demonstration, it should predict the action corresponding to the sampled state. At test time, a full demonstration of a single instance of a new task is presented, and the neural net is expected to perform well on new instances of this new task.
    [Show full text]
  • Reward Learning from Human Preferences and Demonstrations in Atari
    Reward learning from human preferences and demonstrations in Atari Borja Ibarz Jan Leike Tobias Pohlen DeepMind DeepMind DeepMind [email protected] [email protected] [email protected] Geoffrey Irving Shane Legg Dario Amodei OpenAI DeepMind OpenAI [email protected] [email protected] [email protected] Abstract To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the agent directly. In this work, we combine two approaches to learning from human feedback: expert demonstrations and trajectory preferences. We train a deep neural network to model the reward function and use its predicted reward to train an DQN-based deep reinforcement learning agent on 9 Atari games. Our approach beats the imitation learning baseline in 7 games and achieves strictly superhuman performance on 2 games without using game rewards. Additionally, we investigate the goodness of fit of the reward model, present some reward hacking problems, and study the effects of noise in the human labels. 1 Introduction Reinforcement learning (RL) has recently been very successful in solving hard problems in domains with well-specified reward functions (Mnih et al., 2015, 2016; Silver et al., 2016). However, many tasks of interest involve goals that are poorly defined or hard to specify as a hard-coded reward. In those cases we can rely on demonstrations from human experts (inverse reinforcement learning, Ng and Russell, 2000; Ziebart et al., 2008), policy feedback (Knox and Stone, 2009; Warnell et al., 2017), or trajectory preferences (Wilson et al., 2012; Christiano et al., 2017).
    [Show full text]
  • Aerobatics Control of Flying Creatures Via Self-Regulated Learning
    Aerobatics Control of Flying Creatures via Self-Regulated Learning JUNGDAM WON, Seoul National University, South Korea JUNGNAM PARK, Seoul National University, South Korea JEHEE LEE∗, Seoul National University, South Korea Fig. 1. The imaginary dragon learned to perform aerobatic maneuvers. The dragon is physically simulated in realtime and interactively controllable. Flying creatures in animated films often perform highly dynamic aerobatic 1 INTRODUCTION maneuvers, which require their extreme of exercise capacity and skillful In animated films, flying creatures such as birds and dragons often control. Designing physics-based controllers (a.k.a., control policies) for perform highly dynamic flight skills, called aerobatics. The skills aerobatic maneuvers is very challenging because dynamic states remain in unstable equilibrium most of the time during aerobatics. Recently, Deep Re- include rapid rotation about three major axes (pitch, yaw, roll), and inforcement Learning (DRL) has shown its potential in constructing physics- a sequence of skills are performed in a consecutive manner to make based controllers. In this paper, we present a new concept, Self-Regulated dramatic effects. The creature manages to complete aerobatic skills Learning (SRL), which is combined with DRL to address the aerobatics con- by using its extreme of exercise capacity and endurance, which trol problem. The key idea of SRL is to allow the agent to take control over make it attractive and create tension in the audience. its own learning using an additional self-regulation policy. The policy allows Designing a physics-based controller for aerobatics is very chal- the agent to regulate its goals according to the capability of the current con- lenging because it requires extremely skillful control.
    [Show full text]
  • Supplemental
    A Proofs of Propositions Lemma 4 Let ✓ ⇥ be some parameter, consider a random variable s , and fix f : ⇥ R, where f(s, ✓) is continuously2 differentiable with respect to ✓ and integrable2S for all ✓.S Assume⇥ ! for some random variable X with finite mean that @ f(s, ✓) X holds almost surely for all ✓. Then: | @✓ | @ @ @✓ E[f(s, ✓)] = E[ @✓ f(s, ✓)] (17) @ 1 1 Proof. @✓ E[f(s, ✓)] = limδ 0 δ (E[f(s, ✓ + δ)] E[f(s, ✓)]) = limδ 0 E[ δ (f(s, ✓ + δ) f(s, ✓))] @ ! @ − @ ! − =limδ 0 E[ @✓ f(s, ⌧(δ))] = E[limδ 0 @✓ f(s, ⌧(δ))] = E[ @✓ f(s, ✓)], where for the third equality the mean! value theorem guarantees the! existence of ⌧(δ) (✓,✓ + δ), and the fourth equality uses the @ 2 dominated convergence theorem where @✓ f(s, ⌧(δ)) X by assumption. Note that generalizing to the multivariate case (i.e. gradients) simply| requires| that the bound be on max @ f(s, ✓) for i @✓i elements i of ✓. Note that most machine learning models (and energy-based models)| meet/assume| these regularity conditions or similar variants; see e.g. discussion presented in Section 18.1 in [68]. Proposition 1 (Surrogate Objective) Define the “occupancy” loss ⇢ as the difference in energy: L . ⇢(✓) = Es ⇢D E✓(s) Es ⇢✓ E✓(s) (11) L ⇠ − ⇠ Then ✓ ⇢(✓)= Es ⇢D ✓ log ⇢✓(s). In other words, differentiating this recovers the first term in r L − ⇠ r . Equation 9. Therefore if we define a standard “policy” loss ⇡(✓) = Es,a ⇢D log ⇡✓(a s), then: L − ⇠ | . (✓) = (✓)+ (✓) (12) Lsurr L⇢ L⇡ yields a surrogate objective that can be optimized, instead of the original .
    [Show full text]