A State-of-the-Art Review of Deep Reinforcement Learning Techniques for Real-Time Strategy Games

2021 ◽  
pp. 285-307
Author(s):  
Nesma M. Ashraf ◽  
Reham R. Mostafa ◽  
Rasha H. Sakr ◽  
M. Z. Rashad
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
State Of The Art ◽  
Learning Techniques ◽  
Strategy Games

scholarly journals Applying a Deep Q Network for OpenAIs Car Racing Game

2020 ◽  
Author(s):  
Ali Fakhry
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
State Of The Art ◽  
Learning Techniques ◽  
Car Racing ◽  
Custom Made ◽  
Learning Technique ◽  
Reward Threshold

The applications of Deep Q-Networks are seen throughout the field of reinforcement learning, a large subsect of machine learning. Using a classic environment from OpenAI, CarRacing-v0, a 2D car racing environment, alongside a custom based modification of the environment, a DQN, Deep Q-Network, was created to solve both the classic and custom environments. The environments are tested using custom made CNN architectures and applying transfer learning from Resnet18. While DQNs were state of the art years ago, using it for CarRacing-v0 appears somewhat unappealing and not as effective as other reinforcement learning techniques. Overall, while the model did train and the agent learned various parts of the environment, attempting to reach the reward threshold for the environment with this reinforcement learning technique seems problematic and difficult as other techniques would be more useful.

scholarly journals Real Time Strategy Games: A Reinforcement Learning Approach

2015 ◽  
Vol 54 ◽  
pp. 257-264 ◽  
Author(s):  
Harshit Sethy ◽  
Amit Patel ◽  
Vineet Padmanabhan
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Learning Approach ◽  
Strategy Games

EXPERIMENTS WITH ONLINE REINFORCEMENT LEARNING IN REAL-TIME STRATEGY GAMES

2009 ◽  
Vol 23 (9) ◽  
pp. 855-871 ◽  
Author(s):  
Kresten Toftgaard Andersen ◽  
Yifeng Zeng ◽  
Dennis Dahl Christensen ◽  
Dung Tran
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Strategy Games

scholarly journals Deep Reinforcement Learning for Soft Robotic Applications: Brief Overview with Impending Challenges

2018 ◽  
Author(s):  
Sarthak Bhagat ◽  
Hritwick Banerjee ◽  
Hongliang Ren
Keyword(s):  
Reinforcement Learning ◽  
Degrees Of Freedom ◽  
State Of The Art ◽  
Imitation Learning ◽  
Robotic Systems ◽  
Future Research ◽  
Learning Techniques ◽  
Increasing Trend ◽  
Embodied Intelligence ◽  
Robotic Applications

The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to sprouting of a relatively new yet extremely rewarding sphere of technology. The fusion of current deep reinforcement algorithms with physical advantages of a soft bio-inspired structure certainly directs us to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment to achieve a task they have been assigned. For soft robotics structure possessing countless degrees of freedom, it is often not easy (something not even possible) to formulate mathematical constraints necessary for training a deep reinforcement learning (DRL) agent for the task in hand, hence, we resolve to imitation learning techniques due to ease of manually performing such tasks like manipulation that could be comfortably mimicked by our agent. Deploying current imitation learning algorithms on soft robotic systems have been observed to provide satisfactory results but there are still challenges in doing so. This review article thus posits an overview of various such algorithms along with instances of them being applied to real world scenarios and yielding state-of-the-art results followed by brief descriptions on various pristine branches of DRL research that may be centers of future research in this field of interest.

scholarly journals I2DNet - Design and real-time evaluation of an appearance-based gaze estimation system

2021 ◽  
Vol 14 (4) ◽  
Author(s):  
L R D Murthy ◽  
Siddhi Brahmbhatt ◽  
Somnath Arjun ◽  
Pradipta Biswas
Keyword(s):  
Real Time ◽  
User Study ◽  
State Of The Art ◽  
Estimation Accuracy ◽  
Point Estimate ◽  
Gaze Estimation ◽  
Model Based ◽  
Current State ◽  
Learning Techniques ◽  
Estimation System

Gaze estimation problem can be addressed using either model-based or appearance-based approaches. Model-based approaches rely on features extracted from eye images to fit a 3D eye-ball model to obtain gaze point estimate while appearance-based methods attempt to directly map captured eye images to gaze point without any handcrafted features. Recently, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. However, many appearance-based gaze estimation systems perform well in within-dataset validation but fail to provide the same degree of accuracy in cross-dataset evaluation. Hence, it is still unclear how well the current state-of-the-art approaches perform in real-time in an interactive setting on unseen users. This paper proposes I2DNet, a novel architecture aimed to improve subject-independent gaze estimation accuracy that achieved a state-of-the-art 4.3 and 8.4 degree mean angle error on the MPIIGaze and RT-Gene datasets respectively. We have evaluated the proposed system as a gaze-controlled interface in real-time for a 9-block pointing and selection task and compared it with Webgazer.js and OpenFace 2.0. We have conducted a user study with 16 participants, and our proposed system reduces selection time and the number of missed selections statistically significantly compared to other two systems.

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