no code implementations • 17 Oct 2023 • Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao, Per-Arne Andersen, Svein Anders Tunheim, Rishad Shafik, Alex Yakovlev
In brief, the TA of each clause literal has both an absorbing Exclude- and an absorbing Include state, making the learning scheme absorbing instead of ergodic.
no code implementations • 3 Oct 2023 • Mohamed-Bachir Belaid, Jivitesh Sharma, Lei Jiao, Ole-Christoffer Granmo, Per-Arne Andersen, Anis Yazidi
Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domains.
1 code implementation • 12 Sep 2023 • Ole-Christoffer Granmo, Per-Arne Andersen, Lei Jiao, Xuan Zhang, Christian Blakely, Tor Tveit
A set of variables is the Markov blanket of a random variable if it contains all the information needed for predicting the variable.
no code implementations • 5 Sep 2023 • Sander Riisøen Jyhne, Per-Arne Andersen, Morten Goodwin
This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a novel data enhancement technique using edge detection and segmentation models to improve the boundary quality of segmentation masks.
no code implementations • 25 Apr 2023 • Martin Holen, Per-Arne Andersen, Kristian Muri Knausgård, Morten Goodwin
This paper introduces two learning schemes for distributed agents in Reinforcement Learning (RL) environments, namely Reward-Weighted (R-Weighted) and Loss-Weighted (L-Weighted) gradient merger.
1 code implementation • 26 Mar 2023 • Sander Riisøen Jyhne, Per-Arne Andersen, Morten Goodwin
Contrastive Transformer enables existing contrastive learning techniques, often used for image classification, to benefit dense downstream prediction tasks such as semantic segmentation.
1 code implementation • 3 Oct 2022 • Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
CaiRL also presents the first reinforcement learning toolkit with a built-in JVM and Flash support for running legacy flash games for reinforcement learning research.
no code implementations • 3 Oct 2022 • Per-Arne Andersen, Ole-Christoffer Granmo, Morten Goodwin
We show that the DVQN algorithm is a promising approach for identifying initiation and termination conditions for option-based reinforcement learning.
no code implementations • 3 Oct 2022 • Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines.
1 code implementation • 27 Jul 2019 • Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
If these environment dynamics are adequately learned, a model-based approach is perhaps the most sample efficient method for learning agents to act in an environment optimally.
no code implementations • 23 May 2019 • Jivitesh Sharma, Per-Arne Andersen, Ole-Chrisoffer Granmo, Morten Goodwin
We also propose a new reinforcement learning approach that entails pretraining the network weights of a DQN based agents to incorporate information on the shortest path to the exit.
1 code implementation • 2 Oct 2018 • Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms.
1 code implementation • 15 Aug 2018 • Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games.
no code implementations • 29 Jan 2018 • Per-Arne Andersen
This thesis introduces the use of CapsNet for Q-Learning based game algorithms.
no code implementations • 26 Jan 2018 • Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
This paper introduces the Flash Reinforcement Learning platform (FlashRL) which attempts to fill this gap by providing an environment for thousands of Flash games on a novel platform for Flash automation.
no code implementations • 17 Dec 2017 • Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
We propose a game environment in between Atari 2600 and Starcraft II, particularly targeting Deep Reinforcement Learning algorithm research.
no code implementations • 23 Jun 2016 • Per-Arne Andersen, Christian Kråkevik, Morten Goodwin, Anis Yazidi
As main contribution of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm which is able to approximate a student's skill level based on his performance and consequently suggest adequate assignments.