Playing a FPS Doom Video Game with Deep Visual Reinforcement Learning

2019 ◽  
Vol 53 (3) ◽  
pp. 214-222
Author(s):  
Adil Khan ◽  
Feng Jiang ◽  
Shaohui Liu ◽  
Ibrahim Omara
Keyword(s):  
Reinforcement Learning ◽  
Video Game ◽  
Visual Reinforcement

scholarly journals A Review of Mobile Robot Path Planning Based on Deep Reinforcement Learning Algorithm

2021 ◽  
Vol 2138 (1) ◽  
pp. 012011
Author(s):  
Yanwei Zhao ◽  
Yinong Zhang ◽  
Shuying Wang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robot ◽  
Video Game ◽  
Autonomous Navigation ◽  
Learning Algorithm ◽  
Basic Knowledge ◽  
Target Point ◽  
Reinforcement Learning Algorithm

Abstract Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.

scholarly journals ANALYSIS OF THE APPLICATION OF REINFORCEMENT LEARNING ALGORITHMS ON THE STARCRAFT II VIDEO GAME

2020 ◽  
Vol 11 (4) ◽  
Author(s):  
Leandro Vian ◽  
Marcelo De Gomensoro Malheiros
Keyword(s):  
Reinforcement Learning ◽  
Video Game ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Specific Training ◽  
Board Games ◽  
Learning Techniques ◽  
Technical Issues ◽  
Strategy Game ◽  
Real Time Strategy Game

In recent years Machine Learning techniques have become the driving force behind the worldwide emergence of Artificial Intelligence, producing cost-effective and precise tools for pattern recognition and data analysis. A particular approach for the training of neural networks, Reinforcement Learning (RL), achieved prominence creating almost unbeatable artificial opponents in board games like Chess or Go, and also on video games. This paper gives an overview of Reinforcement Learning and tests this approach against a very popular real-time strategy game, Starcraft II. Our goal is to examine the tools and algorithms readily available for RL, also addressing different scenarios where a neural network can be linked to Starcraft II to learn by itself. This work describes both the technical issues involved and the preliminary results obtained by the application of two specific training strategies, A2C and DQN.

scholarly journals Evaluating the impact of curriculum learning on the training process for an intelligent agent in a video game

2021 ◽  
Vol 24 (68) ◽  
pp. 1-20
Author(s):  
Jorge E Camargo ◽  
Rigoberto Sáenz
Keyword(s):  
Reinforcement Learning ◽  
Video Game ◽  
Intelligent Agent ◽  
Performance Measure ◽  
Training Process ◽  
Learning Technique ◽  
The Mean ◽  
Game Simulation ◽  
The Impact

We want to measure the impact of the curriculum learning technique on a reinforcement training setup, several experiments were designed with different training curriculums adapted for the video game chosen as a case study. Then all were executed on a selected game simulation platform, using two reinforcement learning algorithms, and using the mean cumulative reward as a performance measure. Results suggest that curriculum learning has a significant impact on the training process, increasing training times in some cases, and decreasing them up to 40% percent in some other cases.

scholarly journals Human-level performance in 3D multiplayer games with population-based reinforcement learning

Science ◽  
2019 ◽  
Vol 364 (6443) ◽  
pp. 859-865 ◽  
Author(s):  
Max Jaderberg ◽  
Wojciech M. Czarnecki ◽  
Iain Dunning ◽  
Luke Marris ◽  
Guy Lever ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Video Game ◽  
Single Agent ◽  
Three Dimensional ◽  
Population Based ◽  
Great Success ◽  
Multiplayer Games ◽  
Multiagent Reinforcement Learning ◽  
The World ◽  
Level Performance

Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete with other agents. We used a tournament-style evaluation to demonstrate that an agent can achieve human-level performance in a three-dimensional multiplayer first-person video game, Quake III Arena in Capture the Flag mode, using only pixels and game points scored as input. We used a two-tier optimization process in which a population of independent RL agents are trained concurrently from thousands of parallel matches on randomly generated environments. Each agent learns its own internal reward signal and rich representation of the world. These results indicate the great potential of multiagent reinforcement learning for artificial intelligence research.

Sign in / Sign up

Export Citation Format

Share Document