Playing Atari with Deep Reinforcement Learning 19 Dec 2013 · Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
The paper from 2013 that introduced the DQN algorithm for using Deep Learning with Reinforcement Learning to play Atari game.
[Kapturowski, DeepMind, Sep 2022] "Human-level Atari 200x Faster"
Improving the 2020 Agent57 performance to be more efficeint.
The Glass Bead Game is "a kind of synthesis of human learning"[11] in which themes, such as a musical phrase or a philosophical thought, are stated. As the Game progresses, associations between the themes become deeper and more varied.[11] Although the Glass Bead Game is described lucidly, the rules and mechanics are not explained in detail.
Art is the hook that engages students…. The subjects are familiar so that students have much to recognize but they also contain elements of mystery so students have observations, ideas, and emotions to puzzle over [my emphasis]. (p. 24)
Right, so the modern equivalent would be to design a game or an 3d animation in an intuitive way, yet the integration of pipeline in this systems makes it so that not even experienced professionals in the area cn develop a short film or an interactive experience through art that eases people into coding.
I think we need to do a better job at this. If the system that allowed us to design the processes also taught it to people then we wouldn't have to chose between improving the learning curve and the system there should all be one. why did we stop shipping manuals with our tech..? ahh it was because we stopped caring about what the people that designed the tool thought.
Liang, Machado, Talvite, Bowling - AAMAS 2016 "State of the Art Control of Atari Games Using Shallow Reinforcement Learning"
Response paper to DQN showing that well designed Value Function Approximations can also do well at these complex tasks without the use of Deep Learning
A great paper showing how to think differently about the latest advances in Deep RL. All is not always what it seems!
Liang, Machado, Talvite, Bowling - AAMAS 2016 "State of the Art Control of Atari Games Using Shallow Reinforcement Learning"
A great paper showing how to think differently about the latest advances in Deep RL. All is not always what it seems!
LeBlanc, D. G., & Lee, G. (2021). General Deep Reinforcement Learning in NES Games. Canadian AI 2021. Canadian Artificial Intelligence Association (CAIAC). https://doi.org/10.21428/594757db.8472938b
en 2018 et 2019, Ubisoft a ajouté à chaque jeu un mode éliminant toutes les missions et tous lescombats, appelé « Discovery Tours », dans le but que les professeurs d’Histoire dans les écoles secondairespuissent utiliser ces jeux dans leurs cours.
Pourtant, cesmêmes chercheurs accordent toute leur attention aux jeux vidéo sérieux qui, ils le reconnaissent eux-mêmes,sont souvent « peu motivants », « amusants » et « divertissants » (c’est-à-dire, offrant un engagement et uneimmersion faibles), contrairement aux jeux vidéo commerciaux ou basés sur le divertissement (Davidson, 2008;Gee, 2008; Granic et coll., 2014; Hamari et Koivisto, 2015).
les jeux vidéo basés sur le divertissement offrent des expériences similaires aux jeux vidéo sérieux, et qu’il estdonc probable qu’ils mènent à un apprentissage significatif
Academy Games has always prided itself in the quality of its rules. Most of our rules are taught in stages, allowing you to start playing as soon as possible without needing to read everything. We are very careful about the order we teach rules and rely heavily on graphics and pictures to facilitate understanding. We also include a large number of detailed picture examples, often with 3D renders, that help you understand the context of the rules.
Simulations and games in informal learning contexts This article seems to discuss science learning, which is not my foremost interest, but it does give an example of how informal online learning can be used to allow the learner to explore his or her own interests. It is not specific enough to be of high value but is useful as a preliminary reading that can perhaps inform search terms to use for future research. rting 2/5
This page is associated with Thiagi's interactive lectures, which are characterized in a book that is available via this site. This particular page involves links to types of interaction, such as games, puzzles, and so forth. It would be better to read or review the book, but since that option is not available, I will provide a link here. rating 2/5
Let’s instead design games that help strangers form positive pro-social relationships.
Implications for non-game virtual environments as well...
Researchers have tracked student emotions while using Crystal Island–a game-based learning environment– and used that research to predict how students will react in other learning situations