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Source: paper_references, paper_reference_links, observed 2026-08-15T21:16:00.018724Z
Paper Citation Record · LEDGER
As of 17 August 2026, this Paper Citation Record lists 100 of 246 outbound references and 0 inbound Pith citation observations for arXiv:2505.10330.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: paper_references, paper_reference_links, observed 2026-08-15T21:16:00.018724Z
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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
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100 of 246 outbound references displayed
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Observation 8bb213dc-9450-46f7-b3fd-2b6099c64b29 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Mastering the game of go without human knowledge,
Reference 1
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Observation f1d598b9-b60d-44cb-bcea-26b056cab1bf · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Mastering atari, go, chess and shogi by planning with a learned model,
Reference 2
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Observation c49256e1-822a-4cd1-9957-b794514bb4ab · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Grandmaster level in starcraft ii using multi-agent reinforcement learning,
Reference 3
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Observation c5f77ab1-08bf-4a91-b0e4-b52429619df8 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Dota 2 with Large Scale Deep Reinforcement Learning
Reference 4
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Observation 8549a4f1-5811-4511-98c9-6885b84f8aee · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Agent57: Outperforming the human atari benchmark,
Reference 5
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Observation a9ad6ea3-71c7-494c-9814-82a1b315d9e0 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Reinforcement learning based recommender systems: A survey,
Reference 6
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Observation 8787a233-f3d0-4d8f-9132-a9af6cf52f1d · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Deepmind ai reduces google data centre cooling bill by 40%,
Reference 7
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Observation 646eedef-cc8e-4f49-b633-505c07d9fab7 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Gnu-rl: A precocial reinforcement learning so- lution for building hvac control using a differentiable mpc policy,
Reference 8
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Observation b0a39276-513c-4fb5-9374-9fa80e2814a4 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Magnetic control of tokamak plasmas through deep reinforce- ment learning,
Reference 9
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Observation 7edde538-4863-4b5a-83d5-8bfb448839a6 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Adversarial policies beat superhuman go ais,
Reference 10
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Observation af2d9cac-75a0-4ae1-b2b1-61604bdac3ad · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Adaptation in constant utility non-stationary environments.,
Reference 11
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Observation 074e49ff-b87b-447a-a19c-54a6f7d03619 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Unresolved cited work
Reference 12
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Observation a2369159-0aec-464f-976d-cbb0314a3961 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Impact of timing in post-warning prepositioning decisions on performance measures of disaster management: A 163 real-life application,
Reference 13
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Observation bee051dd-7a95-4a27-a96b-1dbfbd3ba6f1 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Domain randomization for transferring deep neural networks from simulation to the real world,
Reference 14
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Observation f6f5d207-465d-4eeb-ac17-ec1b171158f6 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Learning optimal adap- tation strategies in unpredictable motor tasks,
Reference 15
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Observation f8612988-d5f0-4eef-9623-d2bd9a89fe36 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Reward learning: Reinforcement, incentives, and expectations,
Reference 16
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Observation d62bc700-fd5f-499a-90a3-9ed6e906c8ec · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Towards precision holography
Reference 17
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Observation c23d9383-47bb-428d-a8c1-3c1eb5e75a59 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Deep neural networks for youtube rec- ommendations,
Reference 18
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Observation 1accb39b-d7ba-4401-9899-aa4f85be4955 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Scheduling on a budget: Avoiding stale recommendations with timely updates,
Reference 19
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Observation ab83c578-080a-4238-abbc-def669a488b2 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Catastrophic interference in connectionist net- works: The sequential learning problem,
Reference 20
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Observation 3b8e47fc-3c03-43bf-86b5-4a19971b3148 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Learning to predict by the methods of temporal differences,
Reference 21
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Observation a86c677b-3431-4fd4-b80d-033953f4dd8e · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Analysis of temporal-diffference learning with func- tion approximation,
Reference 22
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Observation 3c6ccfc8-6e62-4ea6-9593-08ac31f9e694 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Human-level control through deep reinforcement learning,
Reference 23
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Observation 0c6c19b7-8dff-4885-a1c6-a42f99cfcad4 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Deep Reinforcement Learning and the Deadly Triad
Reference 24
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Observation f7e9ba2d-1b8c-498f-95b0-416f42011241 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Simple statistical gradient-following algorithms for connectionist reinforcement learning,
Reference 25
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Observation 35b57e3e-b9be-41c7-ba46-c22dc1ce44b0 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Asynchronous methods for deep reinforcement learning,
Reference 26
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Observation a41617e8-6cbd-47b7-b593-472866fd3b01 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures,
Reference 27
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Observation 16062bad-540c-40c8-8a77-2a1b55b195bc · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Continuous control with deep reinforcement learning,
Reference 28
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Observation 945f7532-0f0b-4353-ae67-1ee4d13088d4 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Distributed distributional deterministic policy gradients,
Reference 29
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Observation 62c1d1da-3bcd-4d85-8c46-9edcb8be888e · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation,
Reference 30
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Observation 02d67d68-b000-47bb-a1dc-d36558a3c55f · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Soft actor-critic: Off-policy maxi- mum entropy deep reinforcement learning with a stochastic actor,
Reference 31
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Observation 58588fe6-dc46-4e88-aba3-8b8681a924ef · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Trust region pol- icy optimization,
Reference 32
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Observation 88e42701-3dea-44a9-99d1-2e07398935cd · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Proximal Policy Optimization Algorithms
Reference 33
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Observation dd4d2e19-2898-4e64-bdbc-e559d47f153a · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Dyna, an integrated architecture for learning, planning, and reacting,
Reference 34
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Observation 6bb3b444-52e1-4069-b7d3-3ef783a3f54f · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change First return, then explore,
Reference 35
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Observation 4a0ed717-3cb0-47e0-9674-b2ce1d30610f · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Learning latent dynamics for planning from pixels,
Reference 36
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Observation 6ec25d40-c564-4cfc-9958-99194229e867 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Curious model-building control systems,
Reference 37
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Observation f56d2ea2-50f9-4060-9e51-c18d150b418c · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
Reference 38
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Observation 18c87a44-82aa-4a6c-a073-bbf03641872c · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Recurrent world models facilitate policy evolution,
Reference 39
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Observation ed22c21e-7630-4db0-bb89-f39ec53129d3 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Dream to control: Learning behav- iors by latent imagination,
Reference 40
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Observation 7fc91ebd-9bd0-4bde-b830-847e45b5a062 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Mastering atari with discrete world models,
Reference 41
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Observation f04e09b9-1be3-458e-aa98-d378d4291cd3 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Mastering Diverse Domains through World Models
Reference 42
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Observation 5b345e16-a794-4728-a06a-8084bf0f0700 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
Reference 43
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Observation 67b5deec-6cfc-4b56-b67c-f299ef73d8bd · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Convolutional networks for images, speech, and time series,
Reference 44
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Observation c7d134be-e586-4825-9cd3-1e8f6610bc10 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
Reference 45
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Observation 6f2691ef-5777-46ef-b275-9dfce39284dc · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Dm control: Software and tasks for continuous con- trol,
Reference 46
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Observation 0e902dc0-ae37-426c-976e-cd0e7fdcf3c7 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change The arcade learning environment: An evaluation platform for general agents,
Reference 47
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Observation 373b9b0a-ee95-4973-9969-b4f351d66902 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Policy invariance under reward transforma- tions: Theory and application to reward shaping,
Reference 48
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Observation d46664b3-7be0-4861-9f84-5e1cc92ec8ce · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Self-improving reactive agents based on reinforcement learning, plan- ning and teaching,
Reference 49
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Observation badbb7e8-9f76-4d1f-946d-d40e6b9ec770 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Sample efficient actor-critic with experience replay,
Reference 50
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Observation 63dc1484-2984-4929-8633-a13d189143e0 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Rainbow: Combining improvements in deep reinforcement learn- ing,
Reference 51
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Observation 2c3f1207-1be5-45d3-911d-b2b49a2c82c4 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change A Deeper Look at Experience Replay
Reference 52
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Observation a84b1911-47e9-4e67-8861-60e084505eb1 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Revisiting fundamentals of experience replay,
Reference 53
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Observation 2be1cbd3-8fe3-4fd6-b634-012b09fd6ff4 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Prioritized experience replay,
Reference 54
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Observation 887dd01e-658c-4e6b-b6a4-58075e686d7d · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Prioritized experience replay method based on experience reward,
Reference 55
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Observation bf2d5765-a429-4875-9021-55001a1a28f7 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Model-augmented prioritized experience replay,
Reference 56
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Observation 21827c24-b782-4c12-b54a-9cdff7bc22da · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Prioritized experience replay based on dynamics priority,
Reference 57
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Observation 28880909-9ef5-4f2b-95e2-716081aa983e · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Image augmentation is all you need: Reg- ularizing deep reinforcement learning from pixels,
Reference 58
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Observation bb3261a3-2354-4fab-9fa7-3b8d165774e3 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Temporal difference learning for model pre- dictive control,
Reference 59
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Observation 951eaa83-08f4-44f7-875d-a76e668421e0 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Transfer Learning in Deep Reinforcement Learning: A Survey
Reference 60
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Observation ac750a00-b921-4bb4-b3a7-bbb561a2299d · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Distilling the Knowledge in a Neural Network
Reference 61
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Observation bbfd7cca-92d8-457e-8cdd-354b96510e35 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Knowledge distillation: A survey,
Reference 62
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Observation f5b1d7c9-131f-4cb5-a799-62ab7283ce40 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Teaching on a budget: Agents advising agents in rein- forcement learning,
Reference 63
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Observation 65a7e75e-8a59-4218-8d61-f9c10140c608 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Online transfer learning in reinforcement learning do- mains,
Reference 64
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Observation 3fec6310-43f4-4baf-8400-abe9f017ca53 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change A survey on transfer learning,
Reference 65
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Observation f6b7be33-429f-4471-aff3-7e626e0f478c · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Transfer learning for reinforcement learning domains: A survey.,
Reference 66
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Observation e75e4662-eb63-4180-9447-11933bcccff1 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
Reference 67
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Observation 7d2f22a2-1761-4126-a62a-7f75083599cc · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change A review of novelty detection,
Reference 68
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Observation 558d6a72-f865-4a75-8344-4d1000c6f88c · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Towards a unifying framework for formal theories of novelty,
Reference 69
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Observation d4e83569-863d-413b-aa9a-e99ca2d53c82 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Open-world learning for radically autonomous agents,
Reference 70
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Observation 13fd4986-b491-4ed7-a7f1-c23a1214b0f3 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Mixtbn: A fully test-time adaptation method for visual reinforce- ment learning on robotic manipulation,
Reference 71
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Observation 506e06de-0f6c-4e4c-967b-c2cfd0644945 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Active test-time adaptation: Theoretical analyses and an algorithm,
Reference 72
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Observation d69bb3f9-5363-4488-9c46-4db95f5d3cff · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Unknown sample discovery for source free open set domain adaptation,
Reference 73
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Observation 86a5621a-af8d-4542-bd5d-eff244394314 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Hidden-mode markov decision processes for nonstationary sequential decision making,
Reference 74
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Observation 55f29354-0515-4d56-89dd-d7eb3a83b818 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Choosing search heuristics by non-stationary reinforcement learn- ing,
Reference 75
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Observation 15c88ad6-90e1-4269-8d00-b9cf1aa2b763 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Non-stationary reinforcement learning without prior knowledge: An optimal black-box approach,
Reference 76
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Observation 6474dc5c-ee3b-41bb-88f1-bbf9ddcfd52e · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Near-optimal model- free reinforcement learning in non-stationary episodic mdps,
Reference 77
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Observation 68f75129-2aab-4295-b69a-6b5d0005fe42 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Non-stationary reinforcement learning under general function approximation,
Reference 78
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Observation f5ad77ed-1116-4b98-8967-2c6100a10b5d · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Addressing environment non-stationarity by repeat- ing q-learning updates,
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Observation 4fd715e3-9bdb-4ef1-b845-316ab26f6ef8 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Non-stationary markov decision processes, a worst-case approach using model-based reinforcement learning,
Reference 80
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Observation 9de3e21d-e44e-4e85-a7c4-4eb6ac2ec82a · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Reinforcement learning algorithm for non-stationary environments,
Reference 81
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Observation 575e6f73-7b01-44d9-a2c4-484e39a41db0 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Reactive exploration to cope with non-stationarity in life- long reinforcement learning,
Reference 82
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Observation 3644160b-304e-427c-b30b-62cfa6749b00 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Transfer in reinforcement learning: A framework and a survey,
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Observation 9389818f-b3cd-40fd-98a9-52b142a18d76 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Cross-modal domain adaptation for cost-efficient visual reinforcement learning,
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Observation 7879d77e-0e4d-4cb7-9b88-f95b63e0c972 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Deep reinforcement learning amidst lifelong non- stationarity,
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Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Model-based nov- elty adaptation for open-world ai,
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Observation 139fd434-fe0d-47ba-a6de-b7b3fabcfcf2 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Detecting and adapting to novelty in games,
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Observation 05e3ef52-8c99-4259-a8d1-4afd3b6bea1f · outbound
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Observation d3c11078-a8b5-47ed-a070-7c91546c2ea9 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change An integrated architecture for online adaptation to novelty in open worlds using probabilistic programming and novelty-aware planning,
Reference 89
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Observation 03e8f72d-2128-4d16-828f-768e541c3d4e · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Lifelong machine learning systems: Beyond learning algorithms,
Reference 90
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Observation b634438a-fd1c-4970-9325-a0c36b931914 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Online learning and online convex optimization,
Reference 91
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Observation a3890c45-8d6e-47ea-b98f-efa00d81004f · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Introduction to online convex optimization,
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Observation c74011aa-0093-46f0-b1c4-b3b7358044e2 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Measuring catas- trophic forgetting in neural networks,
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Observation af57c857-b637-430a-9a20-f7357cfd3fd8 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Memory efficient experience replay for streaming learning,
Reference 94
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Observation 333e7cbe-c8be-4d13-8436-cdb184485355 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer
Reference 95
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Observation 0a5f28b3-0f63-4859-a0c3-c6671931149a · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Reinforcement learning with gaussian pro- cesses,
Reference 96
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Observation acd0dfca-ade8-4287-975d-c5ddbb3c3399 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Chevalier-Boisvert, L
Reference 97
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Observation 399c1633-f32f-4532-a424-8730b4830a13 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change A multi-agent simulator for generating novelty in monopoly,
Reference 98
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Observation f026a157-e36f-4327-9ee7-0c6efc7721b6 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Novelty gen- eration framework for ai agents in angry birds style physics games,
Reference 99
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Observation 405165c0-6884-4373-b9d5-c509a3d9a614 · outbound
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Schmidhuber, A possibility for implementing curiosity and boredom in model- building neural controllers, 1991
Reference 100
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