In the last chapter, we revisited the Q-learning algorithm and implemented the Q_Learner class. For the Mountain car environment, we used a multi-dimensional array of shape 51x51x3 to represent the action-value function,
. Note that we had discretized the state space to a fixed number of bins given by the NUM_DISCRETE_BINS configuration parameter (we used 50) . We essentially quantized or approximated the observation with a low-dimensional, discrete representation to reduce the number of possible elements in the n-dimensional array. With such a discretization of the observation/state space, we restricted the possible location of the car to a fixed set of 50 locations and the possible velocity of the car to a fixed set of 50 values. Any other location or velocity value would be approximated to one of those fixed set of values. Therefore, it is possible...
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Hands-On Intelligent Agents with OpenAI Gym
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Hands-On Intelligent Agents with OpenAI Gym
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Overview of this book
Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks.
Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
Table of Contents (12 chapters)
Preface
Free Chapter
Introduction to Intelligent Agents and Learning Environments
Reinforcement Learning and Deep Reinforcement Learning
Getting Started with OpenAI Gym and Deep Reinforcement Learning
Exploring the Gym and its Features
Implementing your First Learning Agent - Solving the Mountain Car problem
Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
Creating Custom OpenAI Gym Environments - CARLA Driving Simulator
Implementing an Intelligent - Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)
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