- TORCS is a continuous control problem. DQN works only for discrete actions, and so it cannot be used in TORCS.
- The initialization is another initialization strategy; you can also use a random uniform initialization with the min and max values of the range specified; another approach is to sample from a Gaussian with a zero mean and a specified sigma value. The interested reader must try these different initializers and compare the agent's performance.
- The abs() function is used in the reward function, as we penalize lateral drift from the center equally on either side (left or right). The first term is the longitudinal speed, and so no abs() function is required.
- The Gaussian noise added to the actions for exploration can be tapered down with episode count, and this can result in smoother driving. Surely, there are many other tricks you can do!
- DDPG is off-policy...
TensorFlow Reinforcement Learning Quick Start Guide
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TensorFlow Reinforcement Learning Quick Start Guide
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Overview of this book
Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving.
The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator.
By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems.
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Current Title:
TensorFlow Reinforcement Learning Quick Start Guide
Table of Contents (11 chapters)
Preface
Free Chapter
Up and Running with Reinforcement Learning
Temporal Difference, SARSA, and Q-Learning
Deep Q-Network
Double DQN, Dueling Architectures, and Rainbow
Deep Deterministic Policy Gradient
Asynchronous Methods - A3C and A2C
Trust Region Policy Optimization and Proximal Policy Optimization
Deep RL Applied to Autonomous Driving
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