A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
K. Murphy
A comprehensive overview of deep reinforcement learning and sequential decision making, covering value-based, policy-based, model-based, multi-agent, and LLM-related methods.
Yi Yu, Liuyi Yao, Yuexiang Xie, et al.
Proposes Agentic Memory (AgeMem), a unified framework integrating long-term and short-term memory management into LLM agent policy via tool-based actions and progressive reinforcement learning.
Hans J. Briegel, Gemma De las Cuevas
Proposes a learning agent model using simulation-based projection via a random walk through a dynamic network of episodic memory clips, linking reinforcement learning and quantum computation.
Kai Olav Ellefsen, Jean-Baptiste Mouret, Jeff Clune
This paper shows that evolving modular neural networks with connection costs reduces catastrophic forgetting, enabling faster learning of new skills while retaining old ones.
Zhisheng Hu, Minghui Zhu, Peng Liu
Proposes an adaptive cyber defense using learning-based POMDP with Thompson sampling to counter multi-stage attacks under unknown exploit likelihoods and impacts.
Rashid Ali, Nurullah Shahin, Yousaf Bin Zikria, et al.
Proposes a deep reinforcement learning (Q-learning) based resource allocation mechanism (iQRA) to optimize channel observation-based MAC protocols in dense WLANs.
Vincent François-Lavet, Peter Henderson, Riashat Islam, et al.
This paper provides a comprehensive introduction to deep reinforcement learning, covering models, algorithms, and techniques with a focus on generalization and practical applications.
Tom Dietterich
Introduces the MAXQ decomposition for hierarchical reinforcement learning, enabling faster convergence via state abstractions and recursively optimal policies.
Supriya V. Mahadevkar, Bharti Khemani, Shruti Patil, et al.
A systematic review of machine learning styles in computer vision, covering supervised, unsupervised, reinforcement, and emerging techniques like zero-shot and self-supervised learning.
Lucian Buşoniu, Robert Babuška, Bart De Schutter
A comprehensive survey of multiagent reinforcement learning, covering formal goals, algorithms, and applications across cooperative, competitive, and mixed settings.
Ron Graham
This paper establishes worst-case bounds on the slowdown caused by list scheduling anomalies in multiprocessor systems.
Hado van Hasselt, Arthur Guez, David Silver
This paper shows that DQN overestimates action values in Atari games and proposes a Double DQN algorithm that reduces overestimation and improves performance.