38 citations · 70 across the 14 of their papers we have counts for
14 papers
Self-Improvements in Modern Agentic Systems: A Survey
Zhe Ren, Yimeng Chen, Dandan Guo +9
Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or…
Qwen-AgentWorld: Language World Models for General Agents
Yuxin Zuo, Zikai Xiao, Li Sheng +30
A world model predicts environment dynamics based on current observations and actions, serving as a core cognitive mechanism for reasoning and planning. In this work, we investigat…
Efficient Reinforcement Learning by Reducing Forgetting with Elephant Activation Functions
Qingfeng Lan, Gautham Vasan, A. Rupam Mahmood
Catastrophic forgetting has remained a significant challenge for efficient reinforcement learning for decades (Ring 1994, Rivest and Precup 2003). While recent works have proposed…
Weight Clipping for Deep Continual and Reinforcement Learning
Mohamed Elsayed, Qingfeng Lan, Clare Lyle +1
Many failures in deep continual and reinforcement learning are associated with increasing magnitudes of the weights, making them hard to change and potentially causing overfitting.…
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
Haque Ishfaq, Yixin Tan, Yu Yang +5
Thompson sampling (TS) is one of the most popular exploration techniques in reinforcement learning (RL). However, most TS algorithms with theoretical guarantees are difficult to im…
Elephant Neural Networks: Born to Be a Continual Learner
Qingfeng Lan, A. Rupam Mahmood
Catastrophic forgetting remains a significant challenge to continual learning for decades. While recent works have proposed effective methods to mitigate this problem, they mainly…
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq, Qingfeng Lan, Pan Xu +4
We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling algor…
Learning to Optimize for Reinforcement Learning
Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan +1
In recent years, by leveraging more data, computation, and diverse tasks, learned optimizers have achieved remarkable success in supervised learning, outperforming classical hand-d…
Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation
Qingfeng Lan, Yangchen Pan, Jun Luo +1
Artificial neural networks are promising for general function approximation but challenging to train on non-independent or non-identically distributed data due to catastrophic forg…
Variational Quantum Soft Actor-Critic
Qingfeng Lan
Quantum computing has a superior advantage in tackling specific problems, such as integer factorization and Simon's problem. For more general tasks in machine learning, by applying…
Predictive Representation Learning for Language Modeling
Qingfeng Lan, Luke Kumar, Martha White +1
To effectively perform the task of next-word prediction, long short-term memory networks (LSTMs) must keep track of many types of information. Some information is directly related…
Model-free Policy Learning with Reward Gradients
Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi +1
Despite the increasing popularity of policy gradient methods, they are yet to be widely utilized in sample-scarce applications, such as robotics. The sample efficiency could be imp…
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
Qingfeng Lan, Yangchen Pan, Alona Fyshe +1
Q-learning suffers from overestimation bias, because it approximates the maximum action value using the maximum estimated action value. Algorithms have been proposed to reduce over…
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
Zichen Zhang, Qingfeng Lan, Lei Ding +3
Counterfactual reasoning is an important paradigm applicable in many fields, such as healthcare, economics, and education. In this work, we propose a novel method to address the is…