AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers | Paper Citation Record

Observation e8e0bd80-46b7-4095-b31e-52f844125d5c · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 1

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Observation 05dda284-faf6-4911-a71d-0d410a77a4aa · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Gemini: A Family of Highly Capable Multimodal Models

Reference 2

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Observation 49da78f8-9557-499f-8629-c96cf50d96bd · outbound

This paper cites Measuring massive multitask language understanding.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Measuring massive multitask language understanding

Reference 3

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Observation bbc0b27e-1e77-473e-8e46-9429c7e20594 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Measuring Mathematical Problem Solving With the MATH Dataset

Reference 4

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Observation e75d7813-8482-4d6c-8f6b-87f9be6a9108 · outbound

This paper cites DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs

Reference 5

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Observation 4434356d-25ae-4953-b72b-1762d949a30e · outbound

This paper cites Language models are few-shot learners.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Language models are few-shot learners

Reference 6

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Observation 9d5b0903-fdf8-430b-8e54-bbf25f0dc599 · outbound

This paper cites General-Purpose In-Context Learning by Meta-Learning Transformers.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers General-Purpose In-Context Learning by Meta-Learning Transformers

Reference 7

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Observation 3a9e2440-3a00-405a-8262-17c0cc20c2eb · outbound

This paper cites A Tutorial on Meta-Reinforcement Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers A Tutorial on Meta-Reinforcement Learning

Reference 8

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Observation 9386996f-6c27-45d4-9bb4-bd3c89165e14 · outbound

This paper cites Learning to reinforcement learn.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Learning to reinforcement learn

Reference 9

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Observation 05e9fb36-fa36-49a0-9cb3-794e5bd0746e · outbound

This paper cites RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Reference 10

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Observation 0141d06f-1926-4f04-a71a-84956b218469 · outbound

This paper cites Meta reinforcement learning as task inference.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Meta reinforcement learning as task inference

Reference 11

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Observation 6d4d9a59-4681-4b4b-8312-dee93cfffec8 · outbound

This paper cites Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability

Reference 12

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Observation 75f82e5a-f91b-4d35-bad6-a970dc0a5299 · outbound

This paper cites Fast adaptation via meta reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Fast adaptation via meta reinforcement learning

Reference 13

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Observation ae957ae0-b31d-4fdc-ac69-280bc4f786f5 · outbound

This paper cites On the effectiveness of fine-tuning versus meta-reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers On the effectiveness of fine-tuning versus meta-reinforcement learning

Reference 14

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Observation 9a7ec438-04e2-4132-9ae2-5c74415452c7 · outbound

This paper cites Investigating multi-task pretraining and generalization in reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Investigating multi-task pretraining and generalization in reinforcement learning

Reference 15

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Observation ae323eb4-673d-462a-9ec0-d3908200396f · outbound

This paper cites Probing transfer in deep reinforcement learning without task engineering.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Probing transfer in deep reinforcement learning without task engineering

Reference 16

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Observation c6689f07-2af1-4734-8e3e-3d3310c15ca7 · outbound

This paper cites Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 17

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Observation 6ddaee37-bae2-4353-a420-4ccf773de6ec · outbound

This paper cites Parameterizing non-parametric meta- reinforcement learning tasks via subtask decomposition.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Parameterizing non-parametric meta- reinforcement learning tasks via subtask decomposition

Reference 18

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Observation 36b52613-82cf-443a-a264-64b9ab0281ee · outbound

This paper cites A generalist agent.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers A generalist agent

Reference 19

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Observation 4778cf66-0123-4d1f-b360-89455572b3cf · outbound

This paper cites Td-mpc2: Scalable, robust world models for continuous control.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Td-mpc2: Scalable, robust world models for continuous control

Reference 20

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Observation 904eedf9-7dcf-45d2-946e-915fca177539 · outbound

This paper cites Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent

Reference 21

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Observation a9aa7c6f-2f97-4aa3-bb3e-59c424a31c54 · outbound

This paper cites Causes and cures for interference in multilingual translation.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Causes and cures for interference in multilingual translation

Reference 22

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Observation 10546290-e15b-4d83-b94a-d562be23b710 · outbound

This paper cites Do current multi-task optimization methods in deep learning even help? Advances in neural information processing systems, 35:13597–13609, 2022.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Do current multi-task optimization methods in deep learning even help? Advances in neural information processing systems, 35:13597–13609, 2022

Reference 23

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Observation 944f304a-9ec7-47e3-a835-bcc17648db2e · outbound

This paper cites A survey on multi-task learning.IEEE Transactions on Knowledge and Data Engineering, 34(12):5586–5609, 2021.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers A survey on multi-task learning.IEEE Transactions on Knowledge and Data Engineering, 34(12):5586–5609, 2021

Reference 24

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Observation e0c3b2eb-2836-4fc3-894b-0a6ed00b3e7a · outbound

This paper cites Multi-task deep reinforcement learning with popart.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Multi-task deep reinforcement learning with popart

Reference 25

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Observation 2e3352f0-beec-4735-8372-ccb96d06b233 · outbound

This paper cites Attention is all you need.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Attention is all you need

Reference 26

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Observation 2f447331-c154-42e9-be41-834f0b3799e4 · outbound

This paper cites POP- Gym: Benchmarking partially observable reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers POP- Gym: Benchmarking partially observable reinforcement learning

Reference 27

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Observation 26a048a4-8529-46cc-8d56-afc4bd020cba · outbound

This paper cites Leveraging procedural generation to benchmark reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Leveraging procedural generation to benchmark reinforcement learning

Reference 28

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Observation 1500257d-40f2-4bc8-87d9-ec565e7886e9 · outbound

This paper cites The arcade learning environment: An evaluation platform for general agents.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers The arcade learning environment: An evaluation platform for general agents

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Observation 15e7bd5d-cd16-4983-9fe3-2524d5b27e33 · outbound

This paper cites BabyAI: First steps towards grounded language learning with a human in the loop.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers BabyAI: First steps towards grounded language learning with a human in the loop

Reference 30

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Observation f4ec06d1-cdc6-42cb-b1a9-c38740e4aa99 · outbound

This paper cites Mastering Diverse Domains through World Models.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Mastering Diverse Domains through World Models

Reference 31

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Observation 69fa1a5f-15f6-4143-ab69-aed23f239655 · outbound

This paper cites Critic regularized regression.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Critic regularized regression

Reference 32

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Observation a0942997-0900-4dcd-8960-e3a8533a98d0 · outbound

This paper cites Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions

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Observation c71c2a29-414d-4a20-8954-c0afeeaacee4 · outbound

This paper cites Stop Regressing: Training Value Functions via Classification for Scalable Deep RL.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Reference 34

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Observation f3783051-a2a2-4173-8796-850d8c7f8e47 · outbound

This paper cites Offline Actor-Critic Reinforcement Learning Scales to Large Models.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Offline Actor-Critic Reinforcement Learning Scales to Large Models

Reference 35

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Observation da8586df-f0c5-4ccb-9a30-e6c1621bedbf · outbound

This paper cites Decision transformer: Reinforcement learning via sequence modeling.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Decision transformer: Reinforcement learning via sequence modeling

Reference 36

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Observation 1476d655-eea4-42df-8706-553ae7a1c6a8 · outbound

This paper cites Multi-game decision transformers.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Multi-game decision transformers

Reference 37

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Observation b56488e7-2e14-4a2b-a26e-35431ad280ba · outbound

This paper cites Contextual Markov Decision Processes.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Contextual Markov Decision Processes

Reference 38

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Observation 5a809e27-e247-47c1-9ff3-1f70f5133f93 · outbound

This paper cites Reinforcement learning, fast and slow.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Reinforcement learning, fast and slow

Reference 39

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Observation 7f8f0c28-4a0b-4e8a-941a-e6e6d9f3924f · outbound

This paper cites A survey of generalisa- tion in deep reinforcement learning, 2022.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers A survey of generalisa- tion in deep reinforcement learning, 2022

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Observation 80d33c75-105c-46b1-9604-129ad6c61152 · outbound

This paper cites Contextualize Me -- The Case for Context in Reinforcement Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Contextualize Me -- The Case for Context in Reinforcement Learning

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Observation fde8b43d-e6ae-4e59-a0dc-e2a52a8f439a · outbound

This paper cites Decoupling exploration and exploitation for meta-reinforcement learning without sacrifices.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Decoupling exploration and exploitation for meta-reinforcement learning without sacrifices

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Observation 23d35b38-4845-437e-88be-e9faec54aec2 · outbound

This paper cites Efficient off-policy meta-reinforcement learning via probabilistic context variables.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Efficient off-policy meta-reinforcement learning via probabilistic context variables

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Observation aa82eda7-892c-4ac7-818f-8f9ccfa7238b · outbound

This paper cites SplAgger: Split Aggregation for Meta-Reinforcement Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers SplAgger: Split Aggregation for Meta-Reinforcement Learning

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Observation f8d59b58-f5a2-450d-b640-b7e6e2ac5490 · outbound

This paper cites In-context Reinforcement Learning with Algorithm Distillation.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers In-context Reinforcement Learning with Algorithm Distillation

Reference 45

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Observation 795ed655-34de-4f8f-90dd-ef47b421d5c5 · outbound

This paper cites Supervised pretraining can learn in-context reinforcement learning.Advances in Neural Information Processing Systems, 36, 2024.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Supervised pretraining can learn in-context reinforcement learning.Advances in Neural Information Processing Systems, 36, 2024

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Observation e1accc20-a672-43d5-a96c-fde454f5b091 · outbound

This paper cites Generalization to New Sequential Decision Making Tasks with In-Context Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Generalization to New Sequential Decision Making Tasks with In-Context Learning

Reference 47

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Observation e3da48a4-0a9f-4e67-aa9b-f9fb8c6aec3f · outbound

This paper cites Cross-episodic curriculum for transformer agents.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Cross-episodic curriculum for transformer agents

Reference 48

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Observation 36832bb8-cf1a-4b4d-8f8f-f8c695a10519 · outbound

This paper cites A Simple Neural Attentive Meta-Learner.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers A Simple Neural Attentive Meta-Learner

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Observation 769ffa6c-7b6c-4cd3-93c8-48ae1742e698 · outbound

This paper cites Transformers are meta-reinforcement learners.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Transformers are meta-reinforcement learners

Reference 50

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Observation 8b551f16-99b3-442f-b236-dc6811fb8fac · outbound

This paper cites Structured State Space Models for In-Context Reinforcement Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Structured State Space Models for In-Context Reinforcement Learning

Reference 51

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Observation 9fa0c36e-b606-4133-8923-867a80f229b4 · outbound

This paper cites Meta-Q-Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Meta-Q-Learning

Reference 52

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Observation d7419ac3-2b53-4f01-a6f7-f0be2b0c8c3c · outbound

This paper cites Recurrent Off-policy Baselines for Memory-based Continuous Control.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Recurrent Off-policy Baselines for Memory-based Continuous Control

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Observation f437edf0-7e58-4821-b559-f89f418ba7ae · outbound

This paper cites Recurrent model-free rl can be a strong baseline for many pomdps, 2022.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Recurrent model-free rl can be a strong baseline for many pomdps, 2022

Reference 54

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Observation 59694b9e-4ca7-435e-b5ea-3798c94b47cc · outbound

This paper cites Human-Timescale Adaptation in an Open-Ended Task Space.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Human-Timescale Adaptation in an Open-Ended Task Space

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Observation 10992ff8-83f8-4003-ad4f-dac0fea02bff · outbound

This paper cites When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment

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Resolution: verified exact; local_arxiv, observed 2026-08-12T18:52:14.981634Z

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Observation e36b5f86-4e2c-4ff3-afc3-9297bc80b22d · outbound

This paper cites AMAGO: Scalable in-context reinforcement learning for adaptive agents.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers AMAGO: Scalable in-context reinforcement learning for adaptive agents

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Observation 9d71639f-e0ec-4165-980d-be6daf29f515 · outbound

This paper cites Adapting Auxiliary Losses Using Gradient Similarity.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Adapting Auxiliary Losses Using Gradient Similarity

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.024598Z

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Observation 4d53ec87-68b4-4876-b5c0-7dde6060fd17 · outbound

This paper cites Gradient surgery for multi-task learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Gradient surgery for multi-task learning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.030095Z

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Observation 68c53fcf-1fcc-419b-8ee0-d902726c25ec · outbound

This paper cites Robust optimization for multilingual translation with imbalanced data.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Robust optimization for multilingual translation with imbalanced data

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:16.216218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 21d8989e-6f91-4701-8c68-b301f92eebad · outbound

This paper cites Gradient vaccine: Investigating and improving multi-task optimization in massively multilingual models.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Gradient vaccine: Investigating and improving multi-task optimization in massively multilingual models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:16.194513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8c9a9c73-7c3c-4fa8-ab11-d80a27965fd7 · outbound

This paper cites Conflict-averse gradient descent for multi-task learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Conflict-averse gradient descent for multi-task learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:16.171388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 300a223b-15e4-4485-a9b4-1a8e27354a8c · outbound

This paper cites Scalarization for multi- task and multi-domain learning at scale.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Scalarization for multi- task and multi-domain learning at scale

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:16.146118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d71cc37b-aeb5-4762-8b9b-5a8435d3a2db · outbound

This paper cites In defense of the unitary scalarization for deep multi-task learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers In defense of the unitary scalarization for deep multi-task learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:16.117141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4760a181-8cd2-43ab-bfc1-cab6de7810a4 · outbound

This paper cites Multi-task reinforcement learning with context-based representations.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Multi-task reinforcement learning with context-based representations

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:16.096948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d34a9fbc-e683-48a0-9b6c-654bb3b5ebe9 · outbound

This paper cites Offline q-learning on diverse multi-task data both scales and generalizes.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Offline q-learning on diverse multi-task data both scales and generalizes

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:16.077206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8bda6a36-5226-4606-a416-47927d01d159 · outbound

This paper cites Sharing Knowledge in Multi-Task Deep Reinforcement Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Sharing Knowledge in Multi-Task Deep Reinforcement Learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.071683Z

Source-reported events for the cited work

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Observation 8d6910c1-9afa-4aff-9214-98165f8c2445 · outbound

This paper cites Garage: A toolkit for reproducible reinforcement learning research.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Garage: A toolkit for reproducible reinforcement learning research

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:16.054393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 19636b8f-941d-4a48-b0f4-4624ffb2b3ed · outbound

This paper cites Some Considerations on Learning to Explore via Meta-Reinforcement Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Some Considerations on Learning to Explore via Meta-Reinforcement Learning

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.083932Z

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Observation 66a26dcc-862f-42ac-a092-41bbe8637ae5 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Model-agnostic meta-learning for fast adaptation of deep networks

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:16.018265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a8d6d15-ec1c-45c4-80fb-75176fcb6dd5 · outbound

This paper cites ProMP: Proximal Meta-Policy Search.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers ProMP: Proximal Meta-Policy Search

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.098293Z

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Observation ad09dbf1-5851-4024-b783-635c4853c9f2 · outbound

This paper cites MAMBA: an effective world model approach for meta-reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers MAMBA: an effective world model approach for meta-reinforcement learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.988252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d311d914-7230-44f8-8685-79c8a18ec79f · outbound

This paper cites Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.957502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3a789aae-e73c-4d19-9027-2d0f5b2d8d87 · outbound

This paper cites Procedural generalization by planning with self- supervised world models.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Procedural generalization by planning with self- supervised world models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.930129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8191e294-8603-496c-ac80-23d6d02ced71 · outbound

This paper cites Continuous control with deep reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Continuous control with deep reinforcement learning

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.122412Z

Source-reported events for the cited work

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Observation 67f86b76-e5df-433a-9d5b-4d957e1c4d7f · outbound

This paper cites Soft Actor-Critic for Discrete Action Settings.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Soft Actor-Critic for Discrete Action Settings

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.128108Z

Source-reported events for the cited work

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Observation 647d8e40-34e7-426c-9e3a-22a911530c30 · outbound

This paper cites Randomized Ensembled Double Q-Learning: Learning Fast Without a Model.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.133486Z

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Observation 374e405b-4b04-45f4-83fb-862ff989b69c · outbound

This paper cites Hyperbolic Discounting and Learning over Multiple Horizons.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Hyperbolic Discounting and Learning over Multiple Horizons

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.139308Z

Source-reported events for the cited work

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Observation 7c7d798d-9207-4edd-bbfb-2bf07d77522f · outbound

This paper cites Distributional reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Distributional reinforcement learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.145455Z

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Observation a80a2690-920f-4dcc-a20e-c6fe2d3e20c5 · outbound

This paper cites A distributional perspective on reinforce- ment learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers A distributional perspective on reinforce- ment learning

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.880787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b3fb6b71-b9bc-4aab-b734-bc63f7e6d827 · outbound

This paper cites Master- ing atari, go, chess and shogi by planning with a learned model.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Master- ing atari, go, chess and shogi by planning with a learned model

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.844007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a7361680-cdc3-4cea-bd0c-5acec0d6d4eb · outbound

This paper cites Muesli: Combining improvements in policy optimization.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Muesli: Combining improvements in policy optimization

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.817027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c2e0771e-0d10-4528-8509-f9777f086280 · outbound

This paper cites Recurrent experience replay in distributed reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Recurrent experience replay in distributed reinforcement learning

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.169131Z

Source-reported events for the cited work

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Observation 40c7ac26-6f61-4c9e-b1b3-44f7f7a374c3 · outbound

This paper cites Proximal Policy Optimization Algorithms.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Proximal Policy Optimization Algorithms

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.174551Z

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Observation 612ce31d-b75e-4b5e-b4b7-a37ca61c0365 · outbound

This paper cites Maximum a Posteriori Policy Optimisation.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Maximum a Posteriori Policy Optimisation

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.180450Z

Source-reported events for the cited work

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Observation 6f3d5b54-4372-4e5f-9c0f-17112c7d1318 · outbound

This paper cites Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.186478Z

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Observation 59901b18-e97b-4a98-8c72-e88d256edae7 · outbound

This paper cites Exponentially weighted imitation learning for batched historical data.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Exponentially weighted imitation learning for batched historical data

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.193218Z

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Observation 166a53ed-4c82-4faa-8d92-a506cafd2059 · outbound

This paper cites Bail: Best-action imitation learning for batch deep reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Bail: Best-action imitation learning for batch deep reinforcement learning

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.766752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 86fb7dca-6b1a-40a3-a2b4-e10de2391ae9 · outbound

This paper cites Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.203248Z

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Observation 7d07980a-ae61-4c9e-a9c3-629792bcc40f · outbound

This paper cites A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.208957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0968bcb-c780-4cea-8a85-0db55f8dc90c · outbound

This paper cites Overcoming exploration in reinforcement learning with demonstrations.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Overcoming exploration in reinforcement learning with demonstrations

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.738491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cfaed87a-5deb-42f3-81e0-b1999934b1d8 · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.220716Z

Source-reported events for the cited work

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Observation 9b5c2928-7c13-40ad-bd99-74e1eaadfe28 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.226128Z

Source-reported events for the cited work

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Observation 118212b6-04ea-458e-b307-1c7c300ca58c · outbound

This paper cites Revisiting fundamentals of experience replay.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Revisiting fundamentals of experience replay

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.716815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c542338d-73c0-42c7-b8ab-5699965990a6 · outbound

This paper cites Reinforcement learning as one big sequence modeling problem.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Reinforcement learning as one big sequence modeling problem

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.695060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8862e074-d44a-4521-a4bf-34ddff2549f7 · outbound

This paper cites You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.243975Z

Source-reported events for the cited work

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Observation ea4c9466-2d31-4be8-96e1-dbb92d1eec48 · outbound

This paper cites Hierarchical Transformers are Efficient Meta-Reinforcement Learners.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Hierarchical Transformers are Efficient Meta-Reinforcement Learners

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.250302Z

Source-reported events for the cited work

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Observation 8004eaeb-663d-43da-baee-28a98857afbf · outbound

This paper cites Learning phrase representations using rnn encoder– decoder for statistical machine translation.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Learning phrase representations using rnn encoder– decoder for statistical machine translation

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.673193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2213d364-c7d0-4ce6-bd32-3548ea3f78c8 · outbound

This paper cites Reinforcement learning with fast and forgetful memory.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Reinforcement learning with fast and forgetful memory

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:52:15.649192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 216c0b54-1e8e-4a13-b209-4a5c246ed9f7 · outbound

This paper cites Rainbow: Combining improvements in deep reinforcement learning.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Rainbow: Combining improvements in deep reinforcement learning

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:14.267276Z

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