Agent learning of FilmTrust dataset across various parameters. by Muhammad Waqar (500788)

Single agent and multi-agents tasks for <i>LazyAct</i>. by Hongjie Zhang (136127)

“…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”

The agent-environment interaction in reinforcement learning [3]. by Raed Alharthi (18340157)

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Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

Agent learning of ML100K dataset across various parameters. by Muhammad Waqar (500788)

Agent learning of ML latest-small dataset across various parameters. by Muhammad Waqar (500788)

SIPPO Algorithm for Continuous-Space Facility Location by Mack Jie (21536510)

Network architectures for multi-agents task. by Hongjie Zhang (136127)

“…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”

Time(s) and GFLOPs savings of single-agent tasks. by Hongjie Zhang (136127)

“…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”

Hyperparameters for all agents. by Hasan Raza Khanzada (22404835)

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Comparison of RL agents. by Hasan Raza Khanzada (22404835)

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A parabolic relationship between prediction errors and learning rates obtained by a cubic learning algorithm. by Boluwatife Ikwunne (22238697)

Scores vs Skip ratios on single-agent task. by Hongjie Zhang (136127)

“…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”

Data and code resources. by Sam Hall-McMaster (10343795)

Comparison of PID and RL agents. by Hasan Raza Khanzada (22404835)

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Evaluation criteria for RL agents. by Hasan Raza Khanzada (22404835)

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Training process for RL agents. by Hasan Raza Khanzada (22404835)

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Win rate vs Skip ratios on multi-agents tasks. by Hongjie Zhang (136127)

“…Specifically, each decision made by an agent requires a complete neural network computation, leading to a linear increase in computational cost with the number of interactions and agents. …”

Deep reinforcement learning main components that enable the estimation of the expected reward of any action of the action space of the agent conditioned to any state perceived by t... by Alejandra de-la-Rica-Escudero (20570535)

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Completion times for different algorithms. by Jianbin Zheng (587000)

“…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. …”