Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour | Paper Citation Record

Observation abb96bfa-0dbe-4932-a438-bd80b8f69325 · outbound

This paper cites It refers to the process of determining a path or set of movements, that ensures a robot or swarm thoroughly covers a given area or surface.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour It refers to the process of determining a path or set of movements, that ensures a robot or swarm thoroughly covers a given area or surface

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.416073Z

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This paper cites This environment can be either known or unknown.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour This environment can be either known or unknown

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.401502Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7647a79-500a-433c-b29e-5cc1a80a49f8 · outbound

This paper cites Then, the epsilon-greedy method is chosen to improve exploration during training and facilitate the incorporation of the PSO with the first version of the MASAC.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Then, the epsilon-greedy method is chosen to improve exploration during training and facilitate the incorporation of the PSO with the first version of the MASAC

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.371369Z

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Observation a4f81953-5d06-408d-a89c-8b12a926a371 · outbound

This paper cites One of the chosen metrics for the evaluation is the reward value.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour One of the chosen metrics for the evaluation is the reward value

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.355986Z

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Observation 87d4b32d-a796-47b5-a2a8-a8a1fa2f235a · outbound

This paper cites an unresolved cited work.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:45:18.341516Z

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Observation 1d33d3ac-930a-4e54-ba50-e66f028b7dee · outbound

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

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.268937Z

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Observation 1e84e4d5-fd69-4599-8d8e-463e6ec058bb · outbound

This paper cites Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-08T16:45:17.735834Z

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Observation 982cb50b-fa8d-4473-b73c-e76256481ef9 · outbound

This paper cites A multi -agent framework with moos-ivp for autonomous underwater vehicles with sidescan sonar sensors,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour A multi -agent framework with moos-ivp for autonomous underwater vehicles with sidescan sonar sensors,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.326993Z

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Observation 0bb748c7-f6a9-4782-a540-ba2971c3937c · outbound

This paper cites A survey on swarm robotics for area coverage problem,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour A survey on swarm robotics for area coverage problem,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.313006Z

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Observation 9dad8229-07fe-4680-9283-a23270629952 · outbound

This paper cites an unresolved cited work.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:45:18.298630Z

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Observation 8e87ce62-82ee-49ea-980e-15cf8f5defdd · outbound

This paper cites Buşoniu, R.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Buşoniu, R

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.283796Z

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Observation 4062682d-c4bf-42ec-8fb8-c89367072fff · outbound

This paper cites Multi -agent reinforcement learning: A selective overview of theories and algorithms,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Multi -agent reinforcement learning: A selective overview of theories and algorithms,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.194350Z

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Observation 3d6f99fc-0e4d-4d20-b4ea-ec7b5953206e · outbound

This paper cites An empirical investigation of the challenges of real-world reinforcement learning.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour An empirical investigation of the challenges of real-world reinforcement learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T16:45:17.496518Z

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Observation ecbd545d-e1ed-48d2-a25e-6b0b923ec27c · outbound

This paper cites Multi -robot path planning using an improved self -adaptive particle swarm optimization,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Multi -robot path planning using an improved self -adaptive particle swarm optimization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.254248Z

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Observation 61b184c1-99a3-4f71-84d3-1754267870f5 · outbound

This paper cites Group decisions in humans and animals: A survey,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Group decisions in humans and animals: A survey,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.238665Z

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Observation 98b29bea-e15e-4585-8577-d1066e378b5c · outbound

This paper cites From animal collective behaviors to swarm robotic cooperation,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour From animal collective behaviors to swarm robotic cooperation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.224268Z

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Observation 71208a3d-ccbb-4bd7-b78d-b71a8e206246 · outbound

This paper cites Sample efficient multi-agent reinforcement learning with masked reconstruction,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Sample efficient multi-agent reinforcement learning with masked reconstruction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.209389Z

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Observation cf726d11-10d0-4005-ae16-30e368c59876 · outbound

This paper cites Multi-agent reinforcement learning: a critical survey,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Multi-agent reinforcement learning: a critical survey,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.106374Z

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Observation eb51cdbb-c60f-455f-a049-bb008d303251 · outbound

This paper cites A survey and critique of multiagent deep reinforcement learning,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour A survey and critique of multiagent deep reinforcement learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.179774Z

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Observation 9bbe0531-54d1-4944-8e41-f8cce4951621 · outbound

This paper cites Value-based methods [22] estimate the value of each state or state -action pair to derive optimal policies by selecting actions that maximize the cumulative reward.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Value-based methods [22] estimate the value of each state or state -action pair to derive optimal policies by selecting actions that maximize the cumulative reward

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.386370Z

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Observation 4411e184-94d5-43f6-9fe7-321bfd02ef13 · outbound

This paper cites Multi-agent reinforcement learning: A review of challenges and applications,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Multi-agent reinforcement learning: A review of challenges and applications,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.164646Z

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Observation 50b40735-6b42-4ec3-aadd-327bcc2eab2f · outbound

This paper cites Decentralised learning in systems with many, many strategic agents,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Decentralised learning in systems with many, many strategic agents,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.150351Z

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Observation 9cae0347-92ca-4334-8e15-edb8f0d13012 · outbound

This paper cites An efficient centralized multi-agent reinforcement learner for cooperative tasks,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour An efficient centralized multi-agent reinforcement learner for cooperative tasks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.136007Z

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Observation 7952996a-9bae-4e78-a30c-5605b8f36e03 · outbound

This paper cites Fully decentralized cooperative multi-agent reinforcement learning: A survey,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Fully decentralized cooperative multi-agent reinforcement learning: A survey,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.121258Z

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Observation d7568734-782b-465a-a9d2-7e59c2024b0f · outbound

This paper cites Entropy regularized actor-critic based multi-agent deep reinforcement learning for stochastic games,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Entropy regularized actor-critic based multi-agent deep reinforcement learning for stochastic games,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.092185Z

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Observation a5ebed8b-25bf-4813-9c02-f66a98935604 · outbound

This paper cites Soft Actor-Critic Algorithms and Applications.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Soft Actor-Critic Algorithms and Applications

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T16:45:17.559339Z

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Observation 219ad0d0-9e7b-49e4-ab94-d283b730e6cb · outbound

This paper cites Evaluation of a deep reinforcement-learning-based controller for the control of an autonomous underwater vehicle,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Evaluation of a deep reinforcement-learning-based controller for the control of an autonomous underwater vehicle,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.077725Z

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Observation ad5cc09f-6f48-4f54-9f36-aacab44778e0 · outbound

This paper cites Leveraging world model disentanglement in value-based multi- agent reinforcement learning,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Leveraging world model disentanglement in value-based multi- agent reinforcement learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.062131Z

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Observation b51f8442-ad12-438a-803a-580a3eb11b70 · outbound

This paper cites A collaborative multiagent reinforcement learning method based on policy gradient potential,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour A collaborative multiagent reinforcement learning method based on policy gradient potential,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.046399Z

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Observation c304391f-5f75-4daa-abf3-f7246bd797c8 · outbound

This paper cites Learning to walk via deep reinforcement learning,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Learning to walk via deep reinforcement learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.031577Z

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Observation a5156528-d142-4a9f-8676-e616a80d7efb · outbound

This paper cites Sampling efficient deep reinforcement learning through preference-guided stochastic exploration,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Sampling efficient deep reinforcement learning through preference-guided stochastic exploration,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.016412Z

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Observation 44ed5261-e358-4d88-b6cd-996022d156ab · outbound

This paper cites Heuristic-guided reinforcement learning,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Heuristic-guided reinforcement learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:18.001581Z

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Observation a6c92d13-05da-48d7-8356-3eba463f5a95 · outbound

This paper cites Heuristically accelerated reinforcement learning: Theoretical and experimental results,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Heuristically accelerated reinforcement learning: Theoretical and experimental results,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.986376Z

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Observation 0e201400-f711-4f32-8838-fde2336bf79c · outbound

This paper cites Transferring knowledge as heuristics in reinforcement learning: A case-based approach,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Transferring knowledge as heuristics in reinforcement learning: A case-based approach,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.971416Z

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Observation 64b3559c-c0cd-4759-9a9c-b0d4d9550d3c · outbound

This paper cites Heuristically Accelerated Reinforcement Learning by Means of Case-Based Reasoning and Transfer Learning,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Heuristically Accelerated Reinforcement Learning by Means of Case-Based Reasoning and Transfer Learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.957079Z

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Observation 9b219dd5-5c92-402c-8db3-c7f6dac807c5 · outbound

This paper cites Ant system: optimization by a colony of cooperating agents,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Ant system: optimization by a colony of cooperating agents,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.941959Z

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Observation 747fc2de-80cf-4c29-b5cc-7fcc64b9b29b · outbound

This paper cites The bees algorithm and mechanical design optimisation,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour The bees algorithm and mechanical design optimisation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.926120Z

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Observation d00e7d7d-dd25-4594-a6a1-f147871ebddb · outbound

This paper cites Firefly algorithms for multimodal optimization,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Firefly algorithms for multimodal optimization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.910718Z

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Observation b3145215-8b85-4ce8-9a54-9a5c89c7ca37 · outbound

This paper cites Brain storm optimization algorithm,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Brain storm optimization algorithm,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.894455Z

Source-reported events for the cited work

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Observation 17e15b61-98b5-490c-87ac-3e8251b864e3 · outbound

This paper cites Group search optimizer: An optimization algorithm inspired by animal searching behavior,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Group search optimizer: An optimization algorithm inspired by animal searching behavior,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.879904Z

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Observation 1c4c3b39-6653-428d-9ba4-457f1302fefd · outbound

This paper cites Solving engineering design problems by social cognitive optimization,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Solving engineering design problems by social cognitive optimization,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.864951Z

Source-reported events for the cited work

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Observation 907fce4d-ec6b-4bee-bbc6-ad0d109cd0ef · outbound

This paper cites Particle swarm optimization,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Particle swarm optimization,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.849273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 35ff0bfd-42e2-49bb-b08f-4ce87f845130 · outbound

This paper cites Distributed 3-d path planning for multi-uavs with full area surveillance based on particle swarm optimization,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Distributed 3-d path planning for multi-uavs with full area surveillance based on particle swarm optimization,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.834147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1068253a-6dc0-4ff8-87e7-86fe797f71a9 · outbound

This paper cites Particle swarm optimization algorithm and its applications: A systematic review,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Particle swarm optimization algorithm and its applications: A systematic review,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.817444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 42c7d4af-ad0b-4a18-a990-5014cbe49ef1 · outbound

This paper cites Sim-to-real transfer of adaptive control parameters for auv stabilisation under current disturbance,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Sim-to-real transfer of adaptive control parameters for auv stabilisation under current disturbance,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.800829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7c446ed9-fa46-435f-af65-88d8a34ebd33 · outbound

This paper cites A deeper look at experience replay,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour A deeper look at experience replay,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.785395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e5923bd0-637d-4b4d-b9a4-2a304d0d1442 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.768383Z

Source-reported events for the cited work

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Observation 842c66fc-812d-4e2c-9a54-2e812af0fe69 · outbound

This paper cites Learning adaptive control of a uuv using a bio-inspired experience replay mechanism,.

Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour Learning adaptive control of a uuv using a bio-inspired experience replay mechanism,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:45:17.751603Z

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