DEEP learningWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A ...
vehicular ad hoc network (VANET); reinforcement learning (RL); artificial intelligence (AI); machine learning (ML); wireless networks MSC: 68M181. Introduction Intelligent transport system (ITS) has a great contribution to modern life. This system offers new services to control adverse events ...
As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences between data points, in reinforcement learning the goal is to find a suitable action model that would maximize the total cumulati...
Exercise recommendation is an integral part of enabling personalized learning. Giving appropriate exercises can facilitate learning for learners. The progr
Deep-Q learning on Blackjack Training CFR (chance sampling) on Leduc Hold'em Having fun with pretrained Leduc model Training DMC on Dou Dizhu Evaluating Agents Training Agents on PettingZoo Demo Runexamples/human/leduc_holdem_human.pyto play with the pre-trained Leduc Hold'em model. Leduc Hold...
This project aims to train a machine to strategically play the game of Othello using machine learning. Prior to each match, the machine is trained with dat... S Aharon,A Chang,K Koyanagi 被引量: 0发表: 0年研究点推荐 Reinforcement Learning Othello ...
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log. Topics deep-reinforcement-learning dqn cartpole ddpg sac deep-rl-algorithms ppo a2c lunarlander td3 soft-actor...
Today we’re happy to share an additional milestone involving Project Malmo. Microsoft is partnering with Queen Mary University of London and CrowdAI to co-host a second competition, Learning to Play: The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition. This competition is a ...
. We also developed an BBQ Networks (Bayes-by-Backprop Q-Networks) which performs efficient exploration for dialogue policy learning [Lipton et al. 2017], as well as efficient actor-critic methods which substantially reduce the sample complexity for end-to-end learning of LSTM-based dialogue ...
Batch Reinforcement Learning Supported Environments Note on MuJoCo version Supported Algorithms Citation Contact Disclaimer Benchmarks One of the main challenges when building a research project, or a solution based on a published algorithm, is getting a concrete and reliable baseline that reproduces the...