Driving Simulation Game based on Reinforcement Learning
نویسندگان
چکیده
منابع مشابه
Strategy Acquisition for the Game "Othello" Based on Reinforcement Learning
This article discusses automatic strategy acquisition for the game \Othello" based on reinforcement learning. In our approach, two computer players initially know only the game rules, but they become relatively stronger after playing several thousands of games against each other. In each game, the players re ne the evaluation function for the game state, which is achieved in a reinforcement lea...
متن کاملAn Adaptive Learning Game for Autistic Children using Reinforcement Learning and Fuzzy Logic
This paper, presents an adapted serious game for rating social ability in children with autism spectrum disorder (ASD). The required measurements are obtained by challenges of the proposed serious game. The proposed serious game uses reinforcement learning concepts for being adaptive. It is based on fuzzy logic to evaluate the social ability level of the children with ASD. The game adapts itsel...
متن کاملOperation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm
: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...
متن کاملDeep Reinforcement Learning framework for Autonomous Driving
Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework fo...
متن کاملReinforcement learning based on incompletestate
We construct and examine a network which is able to learn to control a system when parts of the state data from the system sometimes are missing. The network uses reinforcement learning and consists of an already existing agent like the actor-critic network introduced by Barto, Sutton and Anderson Barto et al. 1983] and a novel expectation part. The network builds up an expectation of the next ...
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 for Research in Applied Science and Engineering Technology
سال: 2021
ISSN: 2321-9653
DOI: 10.22214/ijraset.2021.34296