Search Results for author:
Found 14 papers, 8 papers with code
Safe Reinforcement Learning by Imagining the Near Future
Safe reinforcement learning is a promising path toward applying reinforcement learning algorithms to real-world problems, where suboptimal behaviors may lead to actual negative consequences.
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations
This paper explores the possibility of safe RL algorithms with zero training-time safety violations in the challenging setting where we are only given a safe but trivial-reward initial policy without any prior knowledge of the dynamics model and additional offline data.
Towards Learning to Play Piano with Dexterous Hands and Touch
The virtuoso plays the piano with passion, poetry and extraordinary technical ability.
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
Matrix factorization is a simple and natural test-bed to investigate the implicit regularization of gradient descent.
Provable Representation Learning for Imitation Learning via Bi-level Optimization
We formulate representation learning as a bi-level optimization problem where the "outer" optimization tries to learn the joint representation and the "inner" optimization encodes the imitation learning setup and tries to learn task-specific parameters.
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle
Though the idea of using function approximation was proposed at least 60 years ago, even in the simplest setup, i. e, approximating Q-functions with linear functions, it is still an open problem how to design a provably efficient algorithm that learns a near-optimal policy.
On the Expressivity of Neural Networks for Deep Reinforcement Learning
We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, $Q$-functions, and dynamics.
Bootstrapping the Expressivity with Model-based Planning
We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, $Q$-functions, and dynamics.
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling
Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently.
Provably Efficient $Q$-learning with Function Approximation via Distribution Shift Error Checking Oracle
Though the idea of using function approximation was proposed at least 60 years ago, even in the simplest setup, i. e, approximating $Q$-functions with linear functions, it is still an open problem on how to design a provably efficient algorithm that learns a near-optimal policy.
Implicit Regularization in Deep Matrix Factorization
Efforts to understand the generalization mystery in deep learning have led to the belief that gradient-based optimization induces a form of implicit regularization, a bias towards models of low "complexity."
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL.
An online sequence-to-sequence model for noisy speech recognition
This is because the models require that the entirety of the input sequence be available at the beginning of inference, an assumption that is not valid for instantaneous speech recognition.
Learning Online Alignments with Continuous Rewards Policy Gradient
Though capable and easy to use, they require that the entirety of the input sequence is available at the beginning of inference, an assumption that is not valid for instantaneous translation and speech recognition.
