Add to ORKGThis work evaluates competition in training of autonomous agents immersed in First-Person Shooter games using Deep Reinforcement Learning. The agents are composed of a Deep Neural Network, which is trained using Deep Q-Learning. The input of the networks is only the pixels of the screen, allowing the creation of general players, capable of handling several environments without the need for further modifications. ViZDoom, an Application Programming Interface based on the game Doom, is used as the testbed because of its appropriate features. Fifteen agents were divided into three groups, two of which were trained by competing with each other, and the third was trained by competing against opponents that act randomly. The developed agents were...
In the late 2010’s classical games of Go, Chess and Shogi have been considered ’solved’ by deep rei...
The creation of effective autonomous agents (bots) for combat scenarios has long been a goal of the ...
This paper illustrates how we create a software agent by em-ploying FALCON, a self-organizing neural...
Training agents to play in contemporary multiplayer actions game is a challenging task, especially w...
In this work, we study deep reinforcement algorithms forpartially observable Markov decision...
Reinforcement learning (RL) is a paradigm which involves an agent interacting with an environment. T...
Reinforcement learning can be compared to howhumans learn – by interaction, which is the fundamental...
A previous study used the Antarjami gaming framework to determine the OCEAN personality traits. In t...
When interacting with fictional environments, the users' sense of immersion can be broken when chara...
En este proyecto se presenta un modelo de aprendizaje profundo ca- paz de aprender a realizar varia...
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has ...
Reinforcement learning methods allows self-learningagents to play video- and board games autonomousl...
Games are good test-beds to evaluate AI methodologies. In recent years, there has been a vast amount...
Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari game...
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in e...
In the late 2010’s classical games of Go, Chess and Shogi have been considered ’solved’ by deep rei...
The creation of effective autonomous agents (bots) for combat scenarios has long been a goal of the ...
This paper illustrates how we create a software agent by em-ploying FALCON, a self-organizing neural...
Training agents to play in contemporary multiplayer actions game is a challenging task, especially w...
In this work, we study deep reinforcement algorithms forpartially observable Markov decision...
Reinforcement learning (RL) is a paradigm which involves an agent interacting with an environment. T...
Reinforcement learning can be compared to howhumans learn – by interaction, which is the fundamental...
A previous study used the Antarjami gaming framework to determine the OCEAN personality traits. In t...
When interacting with fictional environments, the users' sense of immersion can be broken when chara...
En este proyecto se presenta un modelo de aprendizaje profundo ca- paz de aprender a realizar varia...
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has ...
Reinforcement learning methods allows self-learningagents to play video- and board games autonomousl...
Games are good test-beds to evaluate AI methodologies. In recent years, there has been a vast amount...
Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari game...
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in e...
In the late 2010’s classical games of Go, Chess and Shogi have been considered ’solved’ by deep rei...
The creation of effective autonomous agents (bots) for combat scenarios has long been a goal of the ...
This paper illustrates how we create a software agent by em-ploying FALCON, a self-organizing neural...