|
|
|
|
| LEADER |
09920nmm a2200457 u 4500 |
| 001 |
EB001923618 |
| 003 |
EBX01000000000000001086520 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210123 ||| eng |
| 020 |
|
|
|a 9781788835138
|
| 050 |
|
4 |
|a Q325.5
|
| 100 |
1 |
|
|a Palanisamy, Praveen
|
| 245 |
0 |
0 |
|a Hands-on intelligent agents with OpenAI Gym
|b a step-by-step guide to develop AI agents using deep reinforcement learning
|c Praveen Palanisamy
|
| 260 |
|
|
|a Birmingham, UK
|b Packt Publishing
|c 2018
|
| 300 |
|
|
|a 1 volume
|b illustrations
|
| 505 |
0 |
|
|a Managing hyperparameters and configuration parameters -- Using a JSON file to easily configure parameters -- The parameters manager -- A complete deep Q-learner to solve complex problems with raw pixel input -- The Atari Gym environment -- Customizing the Atari Gym environment -- Implementing custom Gym environment wrappers -- Reward clipping -- Preprocessing Atari screen image frames -- Normalizing observations -- Random no-ops on reset -- Fire on reset -- Episodic life -- Max and skip-frame -- Wrapping the Gym environment -- Training the deep Q-learner to play Atari games -- Putting together a comprehensive deep Q-learner -- Hyperparameters -- Launching the training process -- Testing performance of your deep Q-learner in Atari games -- Summary -- Chapter 7: Creating Custom OpenAI Gym Environments -- CARLA Driving Simulator -- Understanding the anatomy of Gym environments -- Creating a template for custom Gym environment implementations -- Registering custom environments with OpenAI Gym -- Creating an OpenAI Gym-compatible CARLA driving simulator environment -- Configuration and initialization -- Configuration -- Initialization -- Implementing the reset method -- Customizing the CARLA simulation using the CarlaSettings object -- Adding cameras and sensors to a vehicle in CARLA -- Implementing the step function for the CARLA environment -- Accessing camera or sensor data -- Sending actions to control agents in CARLA -- Continuous action space in CARLA -- Discrete action space in CARLA -- Sending actions to the CARLA simulation server -- Determining the end of episodes in the CARLA environment -- Testing the CARLA Gym environment -- Summary -- Chapter 8: Implementing an Intelligent -- Autonomous Car Driving Agent using Deep Actor-Critic Algorithm -- The deep n-step advantage actor-critic algorithm -- Policy gradients -- The likelihood ratio trick
|
| 505 |
0 |
|
|a The policy gradient theorem -- Actor-critic algorithm -- Advantage actor-critic algorithm -- n-step advantage actor-critic algorithm -- n-step returns -- Implementing the n-step return calculation -- Deep n-step advantage actor-critic algorithm -- Implementing a deep n-step advantage actor critic agent -- Initializing the actor and critic networks -- Gathering n-step experiences using the current policy -- Calculating the actor's and critic's losses -- Updating the actor-critic model -- Tools to save/load, log, visualize, and monitor -- An extension -- asynchronous deep n-step advantage actor-critic -- Training an intelligent and autonomous driving agent -- Training and testing the deep n-step advantage actor-critic agent -- Training the agent to drive a car in the CARLA driving simulator -- Summary -- Chapter 9: Exploring the Learning Environment Landscape -- Roboschool, Gym-Retro, StarCraft-II, DeepMindLab -- Gym interface-compatible environments -- Roboschool -- Quickstart guide to setting up and running Roboschool environments -- Gym retro -- Quickstart guide to setup and run Gym Retro -- Other open source Python-based learning environments -- StarCraft II -- PySC2 -- Quick start guide to setup and run StarCraft II PySC2 environment -- Downloading the StarCraft II Linux packages -- Downloading the SC2 maps -- Installing PySC2 -- Playing StarCraftII yourself or running sample agents -- DeepMind lab -- DeepMind Lab learning environment interface -- reset(episode=-1, seed=None) -- step(action, num_steps=1) -- observations() -- is_running() -- observation_spec() -- action_spec() -- num_steps() -- fps() -- events() -- close() -- Quick start guide to setup and run DeepMind Lab -- Setting up and installing DeepMind Lab and its dependencies -- Playing the game, testing a randomly acting agent, or training your own! -- Summary
|
| 505 |
0 |
|
|a Chapter 4: Exploring the Gym and its Features -- Exploring the list of environments and nomenclature -- Nomenclature -- Exploring the Gym environments -- Understanding the Gym interface -- Spaces in the Gym -- Summary -- Chapter 5: Implementing your First Learning Agent -- Solving the Mountain Car problem -- Understanding the Mountain Car problem -- The Mountain Car problem and environment -- Implementing a Q-learning agent from scratch -- Revisiting Q-learning -- Implementing a Q-learning agent using Python and NumPy -- Defining the hyperparameters -- Implementing the Q_Learner class's __init__ method -- Implementing the Q_Learner class's discretize method -- Implementing the Q_Learner's get_action method -- Implementing the Q_learner class's learn method -- Full Q_Learner class implementation -- Training the reinforcement learning agent at the Gym -- Testing and recording the performance of the agent -- A simple and complete Q-Learner implementation for solving the Mountain Car problem -- Summary -- Chapter 6: Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning -- Improving the Q-learning agent -- Using neural networks to approximate Q-functions -- Implementing a shallow Q-network using PyTorch -- Implementing the Shallow_Q_Learner -- Solving the Cart Pole problem using a Shallow Q-Network -- Experience replay -- Implementing the experience memory -- Implementing the replay experience method for the Q-learner class -- Revisiting the epsilon-greedy action policy -- Implementing an epsilon decay schedule -- Implementing a deep Q-learning agent -- Implementing a deep convolutional Q-network in PyTorch -- Using the target Q-network to stabilize an agent's learning -- Logging and visualizing an agent's learning process -- Using TensorBoard for logging and visualizing a PyTorch RL agent's progress
|
| 505 |
0 |
|
|a Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introduction to Intelligent Agents and Learning Environments -- What is an intelligent agent? -- Learning environments -- What is OpenAI Gym? -- Understanding the features of OpenAI Gym -- Simple environment interface -- Comparability and reproducibility -- Ability to monitor progress -- What can you do with the OpenAI Gym toolkit? -- Creating your first OpenAI Gym environment -- Creating and visualizing a new Gym environment -- Summary -- Chapter 2: Reinforcement Learning and Deep Reinforcement Learning -- What is reinforcement learning? -- Understanding what AI means and what's in it in an intuitive way -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Practical reinforcement learning -- Agent -- Rewards -- Environment -- State -- Model -- Value function -- State-value function -- Action-value function -- Policy -- Markov Decision Process -- Planning with dynamic programming -- Monte Carlo learning and temporal difference learning -- SARSA and Q-learning -- Deep reinforcement learning -- Practical applications of reinforcement and deep reinforcement learning algorithms -- Summary -- Chapter 3: Getting Started with OpenAI Gym and Deep Reinforcement Learning -- Code repository, setup, and configuration -- Prerequisites -- Creating the conda environment -- Minimal install -- the quick and easy way -- Complete install of OpenAI Gym learning environments -- Instructions for Ubuntu -- Instructions for macOS -- MuJoCo installation -- Completing the OpenAI Gym setup -- Installing tools and libraries needed for deep reinforcement learning -- Installing prerequisite system packages -- Installing Compute Unified Device Architecture (CUDA) -- Installing PyTorch -- Summary
|
| 505 |
0 |
|
|a Chapter 10: Exploring the Learning Algorithm Landscape -- DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based) -- Deep Deterministic Policy Gradients -- Core concepts -- Proximal Policy Optimization -- Core concept -- Off-policy learning -- On-policy -- Rainbow -- Core concept -- DQN -- Double Q-Learning -- Prioritized experience replay -- Dueling networks -- Multi-step learning/n-step learning -- Distributional RL -- Noisy nets -- Quick summary of advantages and applications -- Summary -- Other Books You May Enjoy -- Index
|
| 653 |
|
|
|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
|
| 653 |
|
|
|a Artificial intelligence / fast
|
| 653 |
|
|
|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
|
| 653 |
|
|
|a Reinforcement learning / fast
|
| 653 |
|
|
|a Intelligence artificielle
|
| 653 |
|
|
|a Machine learning / fast
|
| 653 |
|
|
|a Apprentissage par renforcement (Intelligence artificielle)
|
| 653 |
|
|
|a Apprentissage automatique
|
| 653 |
|
|
|a artificial intelligence / aat
|
| 653 |
|
|
|a COMPUTERS / General / bisacsh
|
| 653 |
|
|
|a Reinforcement learning / http://id.loc.gov/authorities/subjects/sh92000704
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b OREILLY
|a O'Reilly
|
| 776 |
|
|
|z 9781788836579
|
| 776 |
|
|
|z 9781788835138
|
| 776 |
|
|
|z 1788835131
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781788836579/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 006.3/1
|
| 082 |
0 |
|
|a 153.15
|
| 520 |
|
|
|a Walks through the hands-on process of building intelligent agents from the basics and all the way up to solving complex problems including playing Atari games and driving a car autonomously in the CARLA simulator. Discusses various learning environments and how to transform real-world problems into learning environments and solve using the agents
|