This page shows the most relevant public items for Reinforcement Learning, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Mar 4, 2026 • arXiv • Minjune Hwang, Yigit Korkmaz, Daniel Seita, Erdem Bıyık
Reward learning from human preferences often suffers from spurious correlations, leading agents to develop brittle and misaligned behaviors. The authors present a framework that integrates causal i...
Paper • Dec 22, 2025 • arXiv • Percy Liang, Thomas K. V., Eleanor Rigby
Aligning multi-agent systems via traditional human feedback is intractable due to the sheer volume and speed of agent-to-agent interactions. We introduce a novel alignment framework utilizing Inver...
Paper • Jul 15, 2025 • arXiv • Kevin Zhu, Lara Croft, Julian Bao
Evaluating the long-horizon planning and adaptability of Agentic AI in the real world is fraught with safety and cost limitations. We establish Minecraft as the premier sandbox for open-ended agent...
Paper • Jun 7, 2017 • arXiv • Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, Igor Mordatch
We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inhere...
Paper • Jul 5, 2017 • arXiv • Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, Wojciech Zaremba
Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay (HER) which allows sample-efficient lear...
Paper • Jun 21, 2016 • arXiv • Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané
Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper, we discuss one such poten...
Paper • Sep 17, 2019 • arXiv • Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch
We demonstrate that simple multi-agent competition can drive the emergence of highly complex, intelligent behaviors without explicit human design. We train agents using reinforcement learning to pl...
Paper • Jun 12, 2017 • arXiv • Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, Dario Amodei
For many complex real-world tasks, defining a mathematical reward function is difficult, leading to misaligned AI behavior when optimized. We explore a method for solving reinforcement learning tas...
Paper • Jun 23, 2022 • arXiv • Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune
Training agents to perform complex, long-horizon tasks typically requires massive amounts of heavily annotated data or prohibitive amounts of reinforcement learning trial-and-error. We introduce Vi...
Paper • Dec 13, 2019 • arXiv • Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Ilya Sutskever, et al.
We present OpenAI Five, a system of five neural networks that learned to play the highly complex, imperfect-information esports game Dota 2 entirely through self-play. Dota 2 involves long time hor...
Paper • Jul 20, 2017 • arXiv • John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a 'surrogate' objective...
Paper • May 31, 2019 • Science • Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, Thore Graepel
Multiplayer video games represent a significant frontier for AI research, requiring real-time, high-dimensional sensory processing, spatial navigation, and team-based coordination. We report an AI ...
Paper • Feb 16, 2022 • Nature • Jonas Degrave, Federico Felici, Jonas Buchli, Martin Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Jung, Abbas Abdolmaleki, Demis Hassabis, Martin Riedmiller
Nuclear fusion represents a clean, virtually limitless energy source, but sustaining the necessary plasma states inside a tokamak reactor requires complex, high-frequency magnetic control. Traditio...
Paper • Oct 18, 2017 • Nature • David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. We introduce AlphaGo Zero, an AI that achieves superhuman pe...
Paper • Mar 31, 2020 • arXiv • Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Charles Blundell
Atari 2600 games have been a long-standing benchmark in the reinforcement learning community. While previous algorithms have achieved superhuman performance on average, they consistently fail on a ...
Paper • Oct 5, 2022 • Nature • Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Pushmeet Kohli
Matrix multiplication is a fundamental computational task, heavily utilized in neural networks, scientific computing, and graphics. Despite its ubiquity, finding optimal algorithms for matrix multi...
Paper • Jan 27, 2016 • Nature • David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Demis Hassabis
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and move...
Paper • Feb 9, 2018 • arXiv • Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
Scaling reinforcement learning algorithms to utilize thousands of machines efficiently is crucial for tackling complex, visually rich environments. We introduce IMPALA (Importance Weighted Actor-Le...
Paper • Dec 23, 2020 • Nature • Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, David Silver
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challengi...
Paper • Feb 26, 2015 • Nature • Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Demis Hassabis
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural networ...
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