Deep Reinforcement Learning for Goal-Directed Exploration in Sequential Decision Making Scenarios


Deep Reinforcement Learning for Goal-Directed Exploration in Sequential Decision Making Scenarios – In this paper, we propose a novel Deep Reinforcement Learning system, Neural-SteerNet, which can be regarded as a general reinforcement learning system. This system has been tested on a dataset of real-world tasks as well as on a set of tasks with few rewards. We show that the Neural-SteerNet can learn to navigate successfully from a relatively low level problem. Moreover, the network can successfully learn to find the target objects of the task and can navigate, and perform well within the visual environment. Experiments conducted on both real and simulated data illustrate that the Neural-SteerNet can perform better than other reinforcement learning systems on the task and can reach higher accuracies.

We present a novel way to automatically generate actions in a stochastic way, in a continuous sense, and apply it to a variety of human tasks on an arbitrary continuous problem space. We demonstrate that one of the most interesting applications of stochastic reinforcement learning is to automatically generate actions for actions in continuous and continuous sense, which is a promising approach. We present three different ways to generate the actions. We discuss how to use them with the new stochastic reinforcement learning algorithm called Iterative Iterative Learning. Using the Iterative Iterative Learning method we demonstrate how to generate the action actions in continuous and continuous sense by means of a finite state model and a stochastic method. We discuss where to start and how to use the Generative Decision Tree to generate actions in continuous and continuous sense.

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Deep Reinforcement Learning for Goal-Directed Exploration in Sequential Decision Making Scenarios

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  • Learning Feature for RGB-D based Action Recognition and Detection

    Hierarchical Reinforcement Learning in Dynamic Contexts with Decision TreesWe present a novel way to automatically generate actions in a stochastic way, in a continuous sense, and apply it to a variety of human tasks on an arbitrary continuous problem space. We demonstrate that one of the most interesting applications of stochastic reinforcement learning is to automatically generate actions for actions in continuous and continuous sense, which is a promising approach. We present three different ways to generate the actions. We discuss how to use them with the new stochastic reinforcement learning algorithm called Iterative Iterative Learning. Using the Iterative Iterative Learning method we demonstrate how to generate the action actions in continuous and continuous sense by means of a finite state model and a stochastic method. We discuss where to start and how to use the Generative Decision Tree to generate actions in continuous and continuous sense.


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