MARTI-4: new model of human brain, considering neocortex and basal ganglia -- learns to play Atari game by reinforcement learning on a single CPU
Not on the map. Why?
Abstract (arXiv)
We present Deep Control - new ML architecture of cortico-striatal brain circuits, which use whole cortical column as a structural element, instead of a singe neuron. Based on this architecture, we present MARTI - new model of human brain, considering neocortex and basal ganglia. This model is de-signed to implement expedient behavior and is capable to learn and achieve goals in unknown environments. We introduce a novel surprise feeling mechanism, that significantly improves reinforcement learning process through inner rewards. We use OpenAI Gym environment to demonstrate MARTI learning on a single CPU just in several hours.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 18 Aug 2022.
Builds on
6 of 19 references.
References · 6
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
- 2016The Option-Critic Architecturecited by 448
- 2017FeUdal Networks for Hierarchical Reinforcement Learningcited by 337
- 2015Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortexcited by 45
- 2016Feynman Machine: The Universal Dynamical Systems Computercited by 1
Built on by
Not cited yet.