Agents
On Learning Intrinsic Rewards for Policy Gradient Methods
Zeyu Zheng, Junhyuk Oh, Satinder Singh
In this paper we build on the Optimal Rewards Framework of Singh et al. [2010] that defines the optimal intrinsic reward function as one that when used by an RL agent achieves behavior that optimizes the
Depth-Limited Solving for Imperfect-Information Games
Noam Brown, Tuomas Sandholm, Brandon Amos
As a result, depth-limited search algorithms used in single-agent settings and perfect-information games do not apply.
Lifelong Inverse Reinforcement Learning
Jorge Mendez, Shashank Shivkumar, Eric Eaton
Neural Information Processing Systems http://nips.cc/
Multi-Agent Generative Adversarial Imitation Learning
Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon
Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal.
Learning robust controllers that work across many partially observable environments
A rock may block the path, but the robot doesn't know exactly where the rock is. If it did, the problem would be reasonably easy: plan a route around it. But with uncertainty about the obstacle's position, the robot must learn to operate safely and efficiently no matter where the rock turns out to be.
Scalable Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou, Ian Osband, Benjamin Van Roy
Neural Information Processing Systems http://nips.cc/
Designing digital resilience in the agentic AI era
As AI shifts from leveraging information provided by humans to making decisions on their behalf, tech leaders must weave an intelligent data fabric to unlock the full potential of agentic AI while shoring up enterprise-wide resilience. Digital resilience--the ability to prevent, withstand, and recover from digital disruptions--has long been a strategic priority for enterprises. With the rise of agentic AI, the urgency for robust resilience is greater than ever. Agentic AI represents a new generation of autonomous systems capable of proactive planning, reasoning, and executing tasks with minimal human intervention. As these systems shift from experimental pilots to core elements of business operations, they offer new opportunities but also introduce new challenges when it comes to ensuring digital resilience. That's because the autonomy, speed, and scale at which agentic AI operates can amplify the impact of even minor data inconsistencies, fragmentation, or security gaps.
Learning in Games with Lossy Feedback
Zhengyuan Zhou, Panayotis Mertikopoulos, Susan Athey, Nicholas Bambos, Peter W. Glynn, Yinyu Ye
Even though the underlying stage game is fixed (i.e. each
Physics-based Zero-Shot Video Generation Antonio Montanaro
However, it seems that including the dimension of time remains challenging.
