Add to ORKGReinforcement learning is one of the main paradigms of machine learning. In this paradigm learners (agents) are not explicitly told what to do, instead they must on their own explore their environment and determine which actions to take, in order to maximize some numerical signal. Reinforcement learning has several challenges, one of which is the training time. The agents can re- quire millions of training sessions to become proficient at their task, which is troublesome since for most systems it is costly to produce the data. This thesis investigates whether it is beneficial for agents learning to play tag, regarding their proficiency as well as their training speed, to train against other learning agents over non-learning agents. This was...
This work presents a scalable solution to automate game-testing. Traditionally, game-testing has bee...
Reinforcement Learning is a promising approach to develop intelligent agents that can help game deve...
Deep reinforcement learning algorithms typically require large amounts of data to solve a specific p...
Reinforcement learning is one of the main paradigms of machine learning. In this paradigm learners (...
In this thesis, we used deep reinforcement learning to train autonomous agents and evaluated the imp...
In this thesis, we used deep reinforcement learning to train autonomous agents and evaluated the imp...
In this thesis, we used deep reinforcement learning to train autonomous agents and evaluated the imp...
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradie...
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradie...
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradie...
Reinforcement learning is a machine learning field which has received revitalised interest in later ...
Reinforcement learning is a machine learning field which has received revitalised interest in later ...
Reinforcement learning is a machine learning field which has received revitalised interest in later ...
Reinforcement learning is a machine learning field which has received revitalised interest in later ...
This work presents a scalable solution to automate game-testing. Traditionally, game-testing has bee...
This work presents a scalable solution to automate game-testing. Traditionally, game-testing has bee...
Reinforcement Learning is a promising approach to develop intelligent agents that can help game deve...
Deep reinforcement learning algorithms typically require large amounts of data to solve a specific p...
Reinforcement learning is one of the main paradigms of machine learning. In this paradigm learners (...
In this thesis, we used deep reinforcement learning to train autonomous agents and evaluated the imp...
In this thesis, we used deep reinforcement learning to train autonomous agents and evaluated the imp...
In this thesis, we used deep reinforcement learning to train autonomous agents and evaluated the imp...
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradie...
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradie...
In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradie...
Reinforcement learning is a machine learning field which has received revitalised interest in later ...
Reinforcement learning is a machine learning field which has received revitalised interest in later ...
Reinforcement learning is a machine learning field which has received revitalised interest in later ...
Reinforcement learning is a machine learning field which has received revitalised interest in later ...
This work presents a scalable solution to automate game-testing. Traditionally, game-testing has bee...
This work presents a scalable solution to automate game-testing. Traditionally, game-testing has bee...
Reinforcement Learning is a promising approach to develop intelligent agents that can help game deve...
Deep reinforcement learning algorithms typically require large amounts of data to solve a specific p...