Difference between revisions of "Gato"
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* [https://storage.googleapis.com/deepmind-media/A%20Generalist%20Agent/Generalist%20Agent.pdf A Generalist] [[Agents|Agent]] | S. Reed, K. Żołna, E. Parisotto, S. Gómez Colmenarejo, A. Novikov, G. Barth-Maron, M. Giménez, Y. Sulsky, J. Kay, J. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar and N. de Freitas - DeepMind | * [https://storage.googleapis.com/deepmind-media/A%20Generalist%20Agent/Generalist%20Agent.pdf A Generalist] [[Agents|Agent]] | S. Reed, K. Żołna, E. Parisotto, S. Gómez Colmenarejo, A. Novikov, G. Barth-Maron, M. Giménez, Y. Sulsky, J. Kay, J. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar and N. de Freitas - DeepMind | ||
* [https://www.louisbouchard.ai/deepmind-gato/ Deepmind's new model Gato is amazing! | Louis Bouchard] | * [https://www.louisbouchard.ai/deepmind-gato/ Deepmind's new model Gato is amazing! | Louis Bouchard] | ||
| + | * [[Policy]] ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]] | ||
| − | DeepMind's “generalist” AI model inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist [[Agents|agent]] beyond the realm of text outputs. The [[Agents|agent]], which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. | + | DeepMind's “generalist” AI model inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist [[Agents|agent]] beyond the realm of text outputs. The [[Agents|agent]], which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist [[policy]]. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. |
Gato has 16 [[Attention]] Heads... | Gato has 16 [[Attention]] Heads... | ||
Revision as of 15:45, 16 April 2023
YouTube search... ...Google search
- Google's Tools and Resources
- Attention Mechanism ...Transformer Model ...Generative Pre-trained Transformer (GPT)
- A Generalist Agent | S. Reed, K. Żołna, E. Parisotto, S. Gómez Colmenarejo, A. Novikov, G. Barth-Maron, M. Giménez, Y. Sulsky, J. Kay, J. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar and N. de Freitas - DeepMind
- Deepmind's new model Gato is amazing! | Louis Bouchard
- Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)
DeepMind's “generalist” AI model inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.
Gato has 16 Attention Heads...
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