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
Learning from only real-world collected data can be unrealistic and time consuming in many scenario....
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradie...
Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on re...
We want to measure the impact of the curriculum learning technique on a reinforcement training setup...
We want to measure the impact of the curriculum learning technique on a reinforcement training setup...
Humans tend to learn complex abstract concepts faster if examples are presented in a structured mann...
Humans tend to learn complex abstract concepts faster if examples are presented in a structured mann...
Machine learning has consistently proved to be useful in many applications. An integral facet allowi...
Reinforcement learning has proven successful in games, but suffers from long training times when com...
Warfighters are trained in Behavior Cue Analysis to detect anomalies in their environment amongst se...
Reinforcement learning (RL) has proven successful in games, but suffers from long training times whe...
Virtual training and serious games represent a new paradigm in training for many domains by providin...
Transfer learning in reinforcement learning has been an active area of research over the past decade...
Common approaches to learn complex tasks in reinforcement learning include reward shaping, environme...
In order to perform research using novel systems it is first important to distinguish if the collect...
Learning from only real-world collected data can be unrealistic and time consuming in many scenario....
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradie...
Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on re...
We want to measure the impact of the curriculum learning technique on a reinforcement training setup...
We want to measure the impact of the curriculum learning technique on a reinforcement training setup...
Humans tend to learn complex abstract concepts faster if examples are presented in a structured mann...
Humans tend to learn complex abstract concepts faster if examples are presented in a structured mann...
Machine learning has consistently proved to be useful in many applications. An integral facet allowi...
Reinforcement learning has proven successful in games, but suffers from long training times when com...
Warfighters are trained in Behavior Cue Analysis to detect anomalies in their environment amongst se...
Reinforcement learning (RL) has proven successful in games, but suffers from long training times whe...
Virtual training and serious games represent a new paradigm in training for many domains by providin...
Transfer learning in reinforcement learning has been an active area of research over the past decade...
Common approaches to learn complex tasks in reinforcement learning include reward shaping, environme...
In order to perform research using novel systems it is first important to distinguish if the collect...
Learning from only real-world collected data can be unrealistic and time consuming in many scenario....
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradie...
Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on re...