mlsanityData to 14 Oct 2026

The Alberta Plan for AI Research

arXiv 2208.11173 ↗First posted 23 Aug 2022cs.AI cs.LGcited by 18

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Abstract (arXiv)

Herein we describe our approach to artificial intelligence research, which we call the Alberta Plan. The Alberta Plan is pursued within our research groups in Alberta and by others who are like minded throughout the world. We welcome all who would join us in this pursuit.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 10 of its 44 references that are in the map. First posted on arXiv 23 Aug 2022.

Builds on

10 of 44 references.

References · 10

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  4. 2016Reinforcement Learning with Unsupervised Auxiliary Taskscited by 404
  5. 2020Towards Continual Reinforcement Learning: A Review and Perspectivescited by 274
  6. 2021The Option Keyboard: Combining Skills in Reinforcement Learningcited by 67
  7. 2022Reward-Respecting Subtasks for Model-Based Reinforcement Learningcited by 16
  8. 2022The Quest for a Common Model of the Intelligent Decision Makercited by 16
  9. 2021Learning Agent State Online with Recurrent Generate-and-Testcited by 2
  10. 2022The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agentscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 4; 2025: 4; 2026: 3.

18 papers.

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  1. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 135
  2. 2023Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learningcited by 28
  3. 2024Sample-Efficient Alignment for LLMscited by 16
  4. 2025Rethinking the Foundations for Continual Reinforcement Learningcited by 15
  5. 2024Locality Sensitive Sparse Encoding for Learning World Models Onlinecited by 12
  6. 2024UniZero: Generalized and Efficient Planning with Scalable Latent World Modelscited by 12
  7. 2023Harnessing Discrete Representations For Continual Reinforcement Learningcited by 11
  8. 2022Examining Policy Entropy of Reinforcement Learning Agents for Personalization Taskscited by 3
  9. 2025Toward Agents That Reason About Their Computationcited by 3
  10. 2023Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learningcited by 2
  11. 2023Scaling Goal-based Exploration via Pruning Proto-goalscited by 2
  12. 2023NEOLAF, an LLM-powered neural-symbolic cognitive architecturecited by 2
6 more
  1. 2025Exploration by Random Distribution Distillationcited by 1
  2. 2026Position: Deployed Reinforcement Learning should be Continualcited by 1
  3. 2026Going Beyond State-Reaching: Learning Abstractions for Intrinsically Motivated Option Discoverycited by 1
  4. 2024Meta-Gradient Search Control: A Method for Improving the Efficiency of Dyna-style Planningcited by 0
  5. 2025Lessons from Neuroscience for AI: How integrating Actions, Compositional Structure and Episodic Memory could enable Safe, Interpretable and Human-Like AIcited by 0
  6. 2026A Sense of Proprioception in Language Modelscited by 0