- A replay buffer is used in DQN in order to store past experiences, sample a mini-batch of data from it, and use it to train the agent.
- Target networks help in the stability of the training. This is achieved by keeping an additional neural network whose weights are updated using an exponential moving average of the weights of the main neural network. Alternatively, another approach that is also widely used is to copy the weights of the main neural network to the target network once every few thousand steps or so.
- One frame as the state will not help in the Atari Breakout problem. This is because no temporal information is deductible from one frame only. For instance, in one frame alone, the direction of motion of the ball cannot be obtained. If, however, we stack up multiple frames, the velocity and acceleration of the ball can be ascertained.
- L2 loss is known to overfit...
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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