Robot Navigation through the Deep Q-Learning Algorithm
نویسندگان
چکیده
منابع مشابه
Mobile Robot Navigation Using Dynamic Fuzzy Q-learning
Fuzzy logic is a mathematical approach towards the human way of thinking and learning. Based on if-then rules, we can design fuzzy controllers with the intuitive experience of human beings. However, it is not practical for a designer to find necessary number of rules and determine appropriate parameters by hand. Hence, we incorporate a reinforcement learning method with basic fuzzy rules so tha...
متن کاملUsing Q-Learning and Fuzzy Q-Learning Algorithms for Mobile Robot Navigation in Unknown Environment
One of the standing challenging aspects in mobile robotics is the ability to navigate autonomously. It is a difficult task, which requiring a complete modeling of the environment. This paper presents an intelligent navigation method for an autonomous mobile robot which requires only a scalar signal like a feedback indicating the quality of the applied action. Instead of programming a robot, we ...
متن کامل3D Simulation for Robot Arm Control with Deep Q-Learning
Recent trends in robot arm control have seen a shift towards end-to-end solutions, using deep reinforcement learning to learn a controller directly from raw sensor data, rather than relying on a hand-crafted, modular pipeline. However, the high dimensionality of the state space often means that it is impractical to generate sufficient training data with real-world experiments. As an alternative...
متن کاملParameter Selection for the Deep Q-Learning Algorithm
Over the last several years deep learning algorithms have met with dramatic successes across a wide range of application areas. The recently introduced deep Q-learning algorithm represents the first convincing combination of deep learning with reinforcement learning. The algorithm is able to learn policies for Atari 2600 games that approach or exceed human performance. The work presented here i...
متن کاملMobile Robot Path Planning Based on Improved Q Learning Algorithm
For path planning of mobile robot, the traditional Q learning algorithm easy to fall into local optimum, slow convergence etc. issues, this paper proposes a new greedy strategy, multi-target searching of Q learning algorithm. Don't need to create the environment model, the mobile robot from a single-target searching transform into multitarget searching an unknown environment, firstly, by the dy...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Advanced Engineering Research and Science
سال: 2021
ISSN: 2349-6495,2456-1908
DOI: 10.22161/ijaers.82.35