12 citations · 33 across the 8 of their papers we have counts for
8 papers
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training
Yao Wei, Yanchao Sun, Ruijie Zheng +7
We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dep…
PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training
Rogerio Bonatti, Sai Vemprala, Shuang Ma +3
Robotics has long been a field riddled with complex systems architectures whose modules and connections, whether traditional or learning-based, require significant human expertise…
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
Vivek Borkar, Shuhang Chen, Adithya Devraj +2
The paper concerns the -dimensional stochastic approximation recursion, where is a stochastic process on a general st…
Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface
Shuhang Chen, Xiang Zhang, Xiang Shen +2
Brain-machine interfaces (BMIs) help the disabled restore body functions by translating neural activity into digital commands to control external devices. Neural adaptation, where…
Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation
Shuhang Chen, Adithya Devraj, Andrey Bernstein +1
The ODE method has been a workhorse for algorithm design and analysis since the introduction of the stochastic approximation. It is now understood that convergence theory amounts t…
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation
Shuhang Chen, Adithya M. Devraj, Ana Bušić +1
This paper concerns error bounds for recursive equations subject to Markovian disturbances. Motivating examples abound within the fields of Markov chain Monte Carlo (MCMC) and Rein…
Zap Q-Learning With Nonlinear Function Approximation
Shuhang Chen, Adithya M. Devraj, Fan Lu +2
Zap Q-learning is a recent class of reinforcement learning algorithms, motivated primarily as a means to accelerate convergence. Stability theory has been absent outside of two res…
Zap Q-Learning for Optimal Stopping Time Problems
Shuhang Chen, Adithya M. Devraj, Ana Bušić +1
The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreduc…