博文

TensorFlow Reinforcement Learning Quick Start Guide

图片
(3).Defining the actions of the agent   The agent performs actions to explore the environment. Obtaining this action vector is the primary goal in RL. Ideally, you need to strive to obtain optimal actions. An action is the decision an agent takes in a certain state, s t . Typically, it is represented as a t , where, as before, the subscript t denotes the time instant. The actions that are available to an agent depends on the problem. For instance, an agent in a maze can decide to take a step north, or south, or east, or west. These are called discrete actions, as there are a fixed number of possibilities. On the other hand, for an autonomous car, actions can be the steering angle, throttle value, brake value, and so on, which are called continuous actions as they can take real number values in a bounded range. For example, the steering angle can be 40 degrees from the north-south line, and the throttle can be 60% down, and so on. Thus, actions a t   can be either discrete...
发表评论
阅读全文

TensorFlow Reinforcement Learning Quick Start Guide

(1).Defining the states of the agent In RL parlance, states represent the current situation of the agent. For example, in the previous industrial mobile robot agent case, the state at a given time instant is the location of the robot inside the factory – that is, where it is located, its orientation, or more precisely, the pose of the robot. For a robot that has joints and effectors, the state can also include the precise location of the joints and effectors in a three-dimensional space. For an autonomous car, its state can represent its speed, location on a map, distance to other obstacles, torques on its wheels, the rpm of the engine, and so on. States are usually deduced from sensors in the real world; for instance, the measurement from odometers, LIDARs, radars, and cameras. States can be a one-dimensional vector of real numbers or integers, or two-dimensional camera images, or even higher-dimensional, for instance, three-dimensional voxels. There are really no precise limita...
发表评论
阅读全文

TensorFlow Reinforcement Learning Quick Start Guide

(2).The relationship between an agent and its environment     At a very basic level, RL involves an agent and an environment. An agent is an artificial intelligence entity that has certain goals, must remain vigilant about things that can come in the way of these goals, and must, at the same time, pursue the things that help in the attaining of these goals. An environment is everything that the agent can interact with. Let me explain further with an example that involves an industrial mobile robot. For example, in a setting involving an industrial mobile robot navigating inside a factory, the robot is the agent, and the factory is the environment. The robot has certain pre-defined goals, for example, to move goods from one side of the factory to the other without colliding with obstacles such as walls and/or other robots. The environment is the region available for the robot to navigate and includes all the places the robot can go to, including the obstacles that the robot cou...
发表评论
阅读全文