mlsanityData to 14 Oct 2026

MATE: Solving Contextual Markov Decision Processes with Memory of Accumulated Transition Embeddings

arXiv 2605.17431 ↗First posted 17 May 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

We propose MATE, a simple yet effective memory architecture for solving Contextual Markov Decision Processes (CMDPs), a family of MDPs parameterized by an unobserved context. In CMDPs, an optimal agent can adapt online by maintaining the posterior belief over contexts. MATE replaces this intractable posterior with a sum-aggregated memory, leveraging the posterior's permutation invariance to retain provably sufficient expressiveness. Compared to prior memory architectures, MATE avoids the growing per-step rollout cost of Transformers and the gradient issues commonly associated with Recurrent Neural Networks (RNNs). Extensive evaluations across diverse benchmarks demonstrate that MATE provides clear computational advantages while achieving performance comparable to standard sequence-model baselines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 17 May 2026.

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23 of 39 references.

References · 23

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  6. 2019Root Mean Square Layer Normalizationcited by 1,480
  7. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,204
  8. 2016RL2: Fast Reinforcement Learning via Slow Reinforcement Learningcited by 503
  9. 2019Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variablescited by 389
  10. 2015Contextual Markov Decision Processescited by 220
  11. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 170
  12. 2021Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPscited by 122
11 more
  1. 2023Structured State Space Models for In-Context Reinforcement Learningcited by 111
  2. 2023A Tutorial on Meta-Reinforcement Learningcited by 108
  3. 2023When Do Transformers Shine in RL? Decoupling Memory from Credit Assignmentcited by 63
  4. 20251000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilitiescited by 45
  5. 2023Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theoremcited by 34
  6. 2022Context is Everything: Implicit Identification for Dynamics Adaptationcited by 27
  7. 2024Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modelingcited by 18
  8. 2024Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learningcited by 17
  9. 2023Recurrent Hypernetworks are Surprisingly Strong in Meta-RLcited by 16
  10. 2024AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformerscited by 15
  11. 2025Scaling Algorithm Distillation for Continuous Control with Mambacited by 2

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