Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes | Paper Citation Record

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This paper cites Reinforcement learning in game industry—review, prospects and challenges,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Reinforcement learning in game industry—review, prospects and challenges,

Reference 1

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This paper cites Artificial intelligence, machine learning and deep learning in advanced robotics, a review,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Artificial intelligence, machine learning and deep learning in advanced robotics, a review,

Reference 2

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This paper cites Playing Atari with Deep Reinforcement Learning.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Playing Atari with Deep Reinforcement Learning

Reference 3

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This paper cites Weakly coupled deep q-networks,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Weakly coupled deep q-networks,

Reference 4

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This paper cites Tuning apex dqn: A reinforcement learning based deep q-network algorithm,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Tuning apex dqn: A reinforcement learning based deep q-network algorithm,

Reference 5

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This paper cites Safe reinforcement learning via shielding under partial observability,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Safe reinforcement learning via shielding under partial observability,

Reference 6

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Observation c0c88722-5cad-4be5-be71-14d9dad8da2e · outbound

This paper cites Integrated task and motion planning for safe legged navigation in partially observable environments,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Integrated task and motion planning for safe legged navigation in partially observable environments,

Reference 7

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Observation 2a3f48f8-7f06-47b2-a650-a797f9fb2ba3 · outbound

This paper cites Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,

Reference 8

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This paper cites A definition of continual reinforcement learning,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes A definition of continual reinforcement learning,

Reference 9

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This paper cites Deep reinforcement learning unleashing the power of ai in decision-making,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Deep reinforcement learning unleashing the power of ai in decision-making,

Reference 10

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Observation b554e27a-280c-45c4-89ba-5f6b8de6cda1 · outbound

This paper cites Exploration in deep reinforcement learning: A survey,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Exploration in deep reinforcement learning: A survey,

Reference 11

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This paper cites Memory gym: Partially observable challenges to memory-based agents,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Memory gym: Partially observable challenges to memory-based agents,

Reference 12

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This paper cites Deep rein- forcement learning: A survey,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Deep rein- forcement learning: A survey,

Reference 14

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Observation 5f247310-6da1-4525-b271-3e4b7100aa90 · outbound

This paper cites Partially Observable Markov Decision Processes (POMDPs) and Robotics.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Partially Observable Markov Decision Processes (POMDPs) and Robotics

Reference 15

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This paper cites Recurrent neural networks,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Recurrent neural networks,

Reference 16

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This paper cites Long short-term memory,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Long short-term memory,

Reference 17

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This paper cites Gate-variants of gated recurrent unit (gru) neural networks,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Gate-variants of gated recurrent unit (gru) neural networks,

Reference 18

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Observation 70c446ac-4926-4f2c-9ad1-327e144ea182 · outbound

This paper cites Recurrent prediction model for partially observable mdps,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Recurrent prediction model for partially observable mdps,

Reference 19

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This paper cites Visualizing transformers for nlp: a brief survey,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Visualizing transformers for nlp: a brief survey,

Reference 20

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This paper cites Transformers in vision: A survey,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Transformers in vision: A survey,

Reference 21

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This paper cites Windows deep transformer q-networks: an extended variance reduction architecture for partially observable reinforcement learning,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Windows deep transformer q-networks: an extended variance reduction architecture for partially observable reinforcement learning,

Reference 22

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This paper cites Deep Transformer Q-Networks for Partially Observable Reinforcement Learning.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Deep Transformer Q-Networks for Partially Observable Reinforcement Learning

Reference 23

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This paper cites Deep Recurrent Q-Learning for Partially Observable MDPs.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Deep Recurrent Q-Learning for Partially Observable MDPs

Reference 24

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This paper cites On improving deep reinforcement learning for pomdps,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes On improving deep reinforcement learning for pomdps,

Reference 25

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Observation 46461b7f-00a1-4fad-b3cd-85734e91a628 · outbound

This paper cites Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

Reference 26

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Deep reinforcement learning with bidirectional recurrent neural networks for dynamic spectrum access,

Reference 27

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This paper cites On transforming reinforcement learning with transformers: The development trajectory,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes On transforming reinforcement learning with transformers: The development trajectory,

Reference 28

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This paper cites Transformer in reinforcement learning for decision-making: A survey,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Transformer in reinforcement learning for decision-making: A survey,

Reference 29

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Observation bc4f5edf-f3a8-418b-9dd3-a8428fb3d894 · outbound

This paper cites Attention Is All You Need.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Attention Is All You Need

Reference 30

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This paper cites Decision Transformer: Reinforcement Learning via Sequence Modeling.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Decision Transformer: Reinforcement Learning via Sequence Modeling

Reference 31

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This paper cites Deep Attention Recurrent Q-Network.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Deep Attention Recurrent Q-Network

Reference 32

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This paper cites Towards Interpretable Reinforcement Learning Using Attention Augmented Agents.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Reference 33

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation

Reference 34

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 35

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Offline reinforcement learning as one big sequence modeling problem,

Reference 36

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Online Decision Transformer

Reference 37

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

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Structured State Space Models for In-Context Reinforcement Learning

Reference 38

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This paper cites Mastering Memory Tasks with World Models.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Mastering Memory Tasks with World Models

Reference 39

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Mastering atari with discrete world models,

Reference 40

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This paper cites Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 41

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes RWKV: Reinventing RNNs for the Transformer Era

Reference 42

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Resurrecting Recurrent Neural Networks for Long Sequences

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Efficiently modeling long sequences with structured state spaces,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:23.394090Z

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Observation fec57d69-189a-4b98-b57d-14c3d8f1049d · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Simplified State Space Layers for Sequence Modeling

Reference 45

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions

Reference 46

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Efficiently Modeling Long Sequences with Structured State Spaces

Reference 47

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Longformer: The Long-Document Transformer

Reference 48

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Transformer-XL: Attentive language models beyond a fixed-length context,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:23.380533Z

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Stabilizing Transformers for Reinforcement Learning

Reference 50

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Recurrent Memory Transformer

Reference 51

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Reformer: The Efficient Transformer

Reference 52

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Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Linear transformers are secretly fast weight programmers,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:23.366622Z

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This paper cites Investigating the Role of Feed-Forward Networks in Transformers Using Parallel Attention and Feed-Forward Net Design.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Investigating the Role of Feed-Forward Networks in Transformers Using Parallel Attention and Feed-Forward Net Design

Reference 54

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This paper cites Rethinking transformers in solving pomdps,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Rethinking transformers in solving pomdps,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:23.351037Z

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Observation a7a99566-99ec-4cd8-8365-799994a5d2cb · outbound

This paper cites Rethinking Attention with Performers.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Rethinking Attention with Performers

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Observation 0d42377e-36e6-426d-a376-46d3fb013090 · outbound

This paper cites Pomdp robot domains,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Pomdp robot domains,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:23.322069Z

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Observation 85361ea8-fcb7-41ea-82e3-c8634bc66ce6 · outbound

This paper cites Learning policies for partially observable environments: Scaling up,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Learning policies for partially observable environments: Scaling up,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:23.307086Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f01bb95-6aeb-41f4-8076-af9d547f5e1c · outbound

This paper cites gym-gridverse: Gridworld domains for fully and partially observable reinforcement learning,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes gym-gridverse: Gridworld domains for fully and partially observable reinforcement learning,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:23.337172Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 083fdfd6-0e13-46c0-a7d8-14ce4a391f09 · outbound

This paper cites Solving large pomdps using real time dynamic programming,.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Solving large pomdps using real time dynamic programming,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:23.292669Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1933d1b8-cf1c-4040-9f44-594c111672b1 · outbound

This paper cites On Improving Deep Reinforcement Learning for POMDPs.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes On Improving Deep Reinforcement Learning for POMDPs

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:22.710231Z

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Observation 926b6d1d-17ff-4c89-b562-24cb62e6ba2a · outbound

This paper cites Mastering Atari with Discrete World Models.

Bi-directional Recurrence Improves Transformer in Partially Observable Markov Decision Processes Mastering Atari with Discrete World Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:22.778724Z

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