Zero-Shot Reinforcement Learning Under Partial Observability | Paper Citation Record

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This paper cites Deep reinforcement learning at the edge of the statistical precipice.

Zero-Shot Reinforcement Learning Under Partial Observability Deep reinforcement learning at the edge of the statistical precipice

Reference 1

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This paper cites Hindsight experience replay.

Zero-Shot Reinforcement Learning Under Partial Observability Hindsight experience replay

Reference 2

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This paper cites Rudder: Return decomposition for delayed rewards.

Zero-Shot Reinforcement Learning Under Partial Observability Rudder: Return decomposition for delayed rewards

Reference 3

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This paper cites Optimal control of markov processes with incomplete state information.

Zero-Shot Reinforcement Learning Under Partial Observability Optimal control of markov processes with incomplete state information

Reference 4

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This paper cites Layer Normalization.

Zero-Shot Reinforcement Learning Under Partial Observability Layer Normalization

Reference 5

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This paper cites Reinforcement learning with long short-term memory.

Zero-Shot Reinforcement Learning Under Partial Observability Reinforcement learning with long short-term memory

Reference 6

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Observation 5d7598e5-dc2f-4475-b4e7-6791b449ef44 · outbound

This paper cites Augmented world models facilitate zero-shot dynamics generalization from a single offline environment.

Zero-Shot Reinforcement Learning Under Partial Observability Augmented world models facilitate zero-shot dynamics generalization from a single offline environment

Reference 7

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Observation df418c7a-74d2-42fc-90e0-d4f9e9370ef7 · outbound

This paper cites Successor features for transfer in reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability Successor features for transfer in reinforcement learning

Reference 8

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Observation 807bbc2b-591e-476e-91d1-f08bbd0a2476 · outbound

This paper cites Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint.

Zero-Shot Reinforcement Learning Under Partial Observability Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint

Reference 9

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This paper cites Universal Successor Features Approximators.

Zero-Shot Reinforcement Learning Under Partial Observability Universal Successor Features Approximators

Reference 10

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Observation 0f20e5ef-211b-4ca3-8052-946d5f61aa52 · outbound

This paper cites Language models are few-shot learners.

Zero-Shot Reinforcement Learning Under Partial Observability Language models are few-shot learners

Reference 11

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This paper cites Acting optimally in partially observable stochastic domains.

Zero-Shot Reinforcement Learning Under Partial Observability Acting optimally in partially observable stochastic domains

Reference 12

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Observation 18a9c2a8-f72a-47df-b564-5b3464cbbf7d · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Zero-Shot Reinforcement Learning Under Partial Observability Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 13

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Observation 7cf032f4-5977-4097-86b2-29188d804dcc · outbound

This paper cites Quantifying generalization in reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability Quantifying generalization in reinforcement learning

Reference 14

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Observation 71f1e26c-54b4-4b34-a55c-77caca9acb67 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

Zero-Shot Reinforcement Learning Under Partial Observability Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 15

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This paper cites Improving generalization for temporal difference learning: The successor representation.

Zero-Shot Reinforcement Learning Under Partial Observability Improving generalization for temporal difference learning: The successor representation

Reference 16

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Observation 917a2b4b-1c8a-4327-8789-f3a3e511695e · outbound

This paper cites Facing off world model backbones: Rnns, transformers, and s4.

Zero-Shot Reinforcement Learning Under Partial Observability Facing off world model backbones: Rnns, transformers, and s4

Reference 17

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Observation 5fd46fdc-f1da-44db-a719-a2b49aabea3c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Zero-Shot Reinforcement Learning Under Partial Observability An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 18

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This paper cites Finding structure in time.

Zero-Shot Reinforcement Learning Under Partial Observability Finding structure in time

Reference 19

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This paper cites Contrastive learning as goal-conditioned reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability Contrastive learning as goal-conditioned reinforcement learning

Reference 20

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This paper cites Generalization and Regularization in DQN.

Zero-Shot Reinforcement Learning Under Partial Observability Generalization and Regularization in DQN

Reference 21

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This paper cites Hyperbolic Discounting and Learning over Multiple Horizons.

Zero-Shot Reinforcement Learning Under Partial Observability Hyperbolic Discounting and Learning over Multiple Horizons

Reference 22

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This paper cites A minimalist approach to offline reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability A minimalist approach to offline reinforcement learning

Reference 23

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This paper cites Off-policy deep reinforcement learning without exploration.

Zero-Shot Reinforcement Learning Under Partial Observability Off-policy deep reinforcement learning without exploration

Reference 24

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Observation 868ea262-eba7-4a19-a0e2-33fb4a5319d3 · outbound

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

Zero-Shot Reinforcement Learning Under Partial Observability Amago: Scalable in-context reinforcement learning for adaptive agents

Reference 25

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Observation 19a6fd89-7eff-4f64-8b40-6bb77964c9f3 · outbound

This paper cites Amago-2: Breaking the multi-task barrier in meta-reinforcement learning with transformers.

Zero-Shot Reinforcement Learning Under Partial Observability Amago-2: Breaking the multi-task barrier in meta-reinforcement learning with transformers

Reference 26

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Observation 35a9efee-c19a-4029-87b3-3a21b3c93456 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Zero-Shot Reinforcement Learning Under Partial Observability Efficiently Modeling Long Sequences with Structured State Spaces

Reference 27

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This paper cites On the parameterization and initialization of diagonal state space models.

Zero-Shot Reinforcement Learning Under Partial Observability On the parameterization and initialization of diagonal state space models

Reference 28

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Observation 4e96b70a-c51c-4dac-b4a4-430d444f4b44 · outbound

This paper cites World Models.

Zero-Shot Reinforcement Learning Under Partial Observability World Models

Reference 29

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This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

Zero-Shot Reinforcement Learning Under Partial Observability Dream to Control: Learning Behaviors by Latent Imagination

Reference 30

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This paper cites Learning latent dynamics for planning from pixels.

Zero-Shot Reinforcement Learning Under Partial Observability Learning latent dynamics for planning from pixels

Reference 31

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This paper cites Mastering Atari with Discrete World Models.

Zero-Shot Reinforcement Learning Under Partial Observability Mastering Atari with Discrete World Models

Reference 32

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This paper cites Mastering Diverse Domains through World Models.

Zero-Shot Reinforcement Learning Under Partial Observability Mastering Diverse Domains through World Models

Reference 33

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This paper cites Contextual markov decision processes, 2015.

Zero-Shot Reinforcement Learning Under Partial Observability Contextual markov decision processes, 2015

Reference 34

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Zero-Shot Reinforcement Learning Under Partial Observability Array programming with numpy

Reference 35

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Zero-Shot Reinforcement Learning Under Partial Observability Deep recurrent q-learning for partially observable mdps

Reference 36

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Zero-Shot Reinforcement Learning Under Partial Observability Memory-based control with recurrent neural networks

Reference 37

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Zero-Shot Reinforcement Learning Under Partial Observability Long short-term memory

Reference 38

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Zero-Shot Reinforcement Learning Under Partial Observability Matplotlib: A 2d graphics environment

Reference 39

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Zero-Shot Reinforcement Learning Under Partial Observability Unresolved cited work

Reference 40

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This paper cites Monotonic robust policy optimization with model discrepancy.

Zero-Shot Reinforcement Learning Under Partial Observability Monotonic robust policy optimization with model discrepancy

Reference 41

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Zero-Shot Reinforcement Learning Under Partial Observability Planning and acting in partially observable stochastic domains

Reference 42

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Zero-Shot Reinforcement Learning Under Partial Observability Morel: Model-based offline reinforcement learning

Reference 43

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Observation e2565ba5-0f27-43d9-bbc7-5caaa321bd93 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Zero-Shot Reinforcement Learning Under Partial Observability Adam: A Method for Stochastic Optimization

Reference 44

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Zero-Shot Reinforcement Learning Under Partial Observability Actor-critic algorithms

Reference 45

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This paper cites Stabilizing off-policy q-learning via bootstrapping error reduction.

Zero-Shot Reinforcement Learning Under Partial Observability Stabilizing off-policy q-learning via bootstrapping error reduction

Reference 46

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This paper cites Stabilizing off-policy q-learning via bootstrapping error reduction.

Zero-Shot Reinforcement Learning Under Partial Observability Stabilizing off-policy q-learning via bootstrapping error reduction

Reference 47

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Zero-Shot Reinforcement Learning Under Partial Observability Conservative Q-Learning for Offline Reinforcement Learning

Reference 48

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Zero-Shot Reinforcement Learning Under Partial Observability Batch reinforcement learning

Reference 49

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This paper cites Context-aware dynamics model for generalization in model-based reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability Context-aware dynamics model for generalization in model-based reinforcement learning

Reference 50

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Observation e4664fa5-2a55-44d1-ae18-dab3ef0786dc · outbound

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

Zero-Shot Reinforcement Learning Under Partial Observability Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 51

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This paper cites Off-Policy Policy Gradient with State Distribution Correction.

Zero-Shot Reinforcement Learning Under Partial Observability Off-Policy Policy Gradient with State Distribution Correction

Reference 52

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This paper cites Structured state space models for in-context reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability Structured state space models for in-context reinforcement learning

Reference 53

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Observation fa96089b-dc21-49ce-8d75-5eb1e52daf8a · outbound

This paper cites How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression.

Zero-Shot Reinforcement Learning Under Partial Observability How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression

Reference 54

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Zero-Shot Reinforcement Learning Under Partial Observability Robust Reinforcement Learning for Continuous Control with Model Misspecification

Reference 55

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Zero-Shot Reinforcement Learning Under Partial Observability pandas: a foundational python library for data analysis and statistics

Reference 56

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Zero-Shot Reinforcement Learning Under Partial Observability Memory-based deep reinforcement learning for pomdps

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.756148Z

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This paper cites Steps toward artificial intelligence.

Zero-Shot Reinforcement Learning Under Partial Observability Steps toward artificial intelligence

Reference 58

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Zero-Shot Reinforcement Learning Under Partial Observability Human-level control through deep reinforcement learning

Reference 59

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This paper cites POPGym: Benchmarking Partially Observable Reinforcement Learning.

Zero-Shot Reinforcement Learning Under Partial Observability POPGym: Benchmarking Partially Observable Reinforcement Learning

Reference 60

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Zero-Shot Reinforcement Learning Under Partial Observability Robust reinforcement learning

Reference 61

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Observation c7d8530c-a575-4793-9688-dcc3157acc13 · outbound

This paper cites Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs.

Zero-Shot Reinforcement Learning Under Partial Observability Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs

Reference 62

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Observation 4b262d7c-34f8-46c8-a5b2-fae1dd7b0b27 · outbound

This paper cites Robust control of markov decision processes with uncertain transition matrices.

Zero-Shot Reinforcement Learning Under Partial Observability Robust control of markov decision processes with uncertain transition matrices

Reference 63

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Observation 68186d0b-af03-4957-908b-1541299b95e9 · outbound

This paper cites Assessing Generalization in Deep Reinforcement Learning.

Zero-Shot Reinforcement Learning Under Partial Observability Assessing Generalization in Deep Reinforcement Learning

Reference 64

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This paper cites Stabilizing transformers for reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability Stabilizing transformers for reinforcement learning

Reference 65

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Observation e71ba0c2-2617-423e-a59a-b25b1ca40278 · outbound

This paper cites Hiql: Offline goal-conditioned rl with latent states as actions.

Zero-Shot Reinforcement Learning Under Partial Observability Hiql: Offline goal-conditioned rl with latent states as actions

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.683624Z

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Observation 8e683216-8398-4ecf-b387-9b5f5c20c041 · outbound

This paper cites Foundation policies with hilbert representations.

Zero-Shot Reinforcement Learning Under Partial Observability Foundation policies with hilbert representations

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.668577Z

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Observation 511fde79-6680-448b-840a-3c87033fd7c6 · outbound

This paper cites Automatic differentiation in pytorch.

Zero-Shot Reinforcement Learning Under Partial Observability Automatic differentiation in pytorch

Reference 68

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This paper cites Fast imitation via behavior foundation models.

Zero-Shot Reinforcement Learning Under Partial Observability Fast imitation via behavior foundation models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.644796Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 55abfbbe-c184-4cb9-8ee9-edf199a201e8 · outbound

This paper cites Automatic Data Augmentation for Generalization in Deep Reinforcement Learning.

Zero-Shot Reinforcement Learning Under Partial Observability Automatic Data Augmentation for Generalization in Deep Reinforcement Learning

Reference 70

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Observation 2227ed27-a674-4b16-a5a0-b4056ed2ff5c · outbound

This paper cites EPOpt: Learning Robust Neural Network Policies Using Model Ensembles.

Zero-Shot Reinforcement Learning Under Partial Observability EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

Reference 71

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Observation c2c8a653-fcf0-4190-8bda-e8640f52f4f0 · outbound

This paper cites Synthetic Returns for Long-Term Credit Assignment.

Zero-Shot Reinforcement Learning Under Partial Observability Synthetic Returns for Long-Term Credit Assignment

Reference 72

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This paper cites Reward-Free Curricula for Training Robust World Models.

Zero-Shot Reinforcement Learning Under Partial Observability Reward-Free Curricula for Training Robust World Models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:29.842674Z

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This paper cites High-resolution image synthesis with latent diffusion models.

Zero-Shot Reinforcement Learning Under Partial Observability High-resolution image synthesis with latent diffusion models

Reference 74

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This paper cites Python: a programming language for software integration and development.

Zero-Shot Reinforcement Learning Under Partial Observability Python: a programming language for software integration and development

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.620707Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 376148d4-a25c-4f5f-a7c9-9358faec5095 · outbound

This paper cites Universal value function approximators.

Zero-Shot Reinforcement Learning Under Partial Observability Universal value function approximators

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.605450Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 38fcec82-510a-4a84-9c06-39722501636c · outbound

This paper cites Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions.

Zero-Shot Reinforcement Learning Under Partial Observability Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:29.860626Z

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Observation b01114b7-071b-4cea-91f1-65115677c446 · outbound

This paper cites Reinforcement learning in markovian and non-markovian environments.

Zero-Shot Reinforcement Learning Under Partial Observability Reinforcement learning in markovian and non-markovian environments

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.590304Z

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Observation 1ecd03db-429e-4f0b-95c7-02500b6b485e · outbound

This paper cites Trajectory-wise multiple choice learning for dynamics generalization in reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability Trajectory-wise multiple choice learning for dynamics generalization in reinforcement learning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.574754Z

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Observation 407b2b38-e276-4752-86a2-bfd964238e72 · outbound

This paper cites Temporal credit assignment in reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability Temporal credit assignment in reinforcement learning

Reference 80

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Observation 2a2f8887-3798-40b0-8beb-df1ae25b0f29 · outbound

This paper cites DeepMind Control Suite.

Zero-Shot Reinforcement Learning Under Partial Observability DeepMind Control Suite

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:29.878368Z

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Observation 6d4e7c52-71e6-4868-b5af-3b9062571e5d · outbound

This paper cites Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning.

Zero-Shot Reinforcement Learning Under Partial Observability Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:29.883154Z

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Observation 47b145d8-d437-4e1b-9037-cdcc5700b3f9 · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.

Zero-Shot Reinforcement Learning Under Partial Observability Domain randomization for transferring deep neural networks from simulation to the real world

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:29.888157Z

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This paper cites Mujoco: A physics engine for model-based control.

Zero-Shot Reinforcement Learning Under Partial Observability Mujoco: A physics engine for model-based control

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:29.892663Z

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Observation 966fed24-1e51-4619-a0e3-1cf3138a950e · outbound

This paper cites Learning one representation to optimize all rewards.

Zero-Shot Reinforcement Learning Under Partial Observability Learning one representation to optimize all rewards

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.530779Z

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Observation e5f34fca-cc7e-4d0e-acd8-be2ac9b95955 · outbound

This paper cites Does zero-shot reinforcement learning exist? In The Eleventh International Conference on Learning Representations, 2023.

Zero-Shot Reinforcement Learning Under Partial Observability Does zero-shot reinforcement learning exist? In The Eleventh International Conference on Learning Representations, 2023

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.515552Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation de40c939-25da-42d0-8f94-1af2e11fe73f · outbound

This paper cites Attention is all you need.

Zero-Shot Reinforcement Learning Under Partial Observability Attention is all you need

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:29.905823Z

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This paper cites Active perception and reinforcement learning.

Zero-Shot Reinforcement Learning Under Partial Observability Active perception and reinforcement learning

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.490669Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e5be8a5a-1179-4702-9d08-d29702676e7a · outbound

This paper cites Policy gradient critics.

Zero-Shot Reinforcement Learning Under Partial Observability Policy gradient critics

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.475027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2c43ebbf-cdcb-438d-a90c-89b5e2d43f8b · outbound

This paper cites Meta-gradient reinforcement learning with an objective discovered online.

Zero-Shot Reinforcement Learning Under Partial Observability Meta-gradient reinforcement learning with an objective discovered online

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.460421Z

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Observation 4e439a89-65e0-422c-8c60-62163ed0f744 · outbound

This paper cites Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning.

Zero-Shot Reinforcement Learning Under Partial Observability Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:29.924574Z

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This paper cites Learning deep neural network policies with continuous memory states.

Zero-Shot Reinforcement Learning Under Partial Observability Learning deep neural network policies with continuous memory states

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:30.445202Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 66ed7fcb-7b1b-4894-b12f-6225b0543ca4 · outbound

This paper cites write newline.

Zero-Shot Reinforcement Learning Under Partial Observability write newline

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:29.933624Z

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