research
∙
08/22/2023
Careful at Estimation and Bold at Exploration
Exploration strategies in continuous action space are often heuristic du...
share
research
∙
08/18/2023
Baird Counterexample Is Solved: with an example of How to Debug a Two-time-scale Algorithm
Baird counterexample was proposed by Leemon Baird in 1995, first used to...
research
∙
07/29/2023
A new Gradient TD Algorithm with only One Step-size: Convergence Rate Analysis using L-λ Smoothness
Gradient Temporal Difference (GTD) algorithms (Sutton et al., 2008, 2009...
research
∙
11/25/2022
The Vanishing Decision Boundary Complexity and the Strong First Component
We show that unlike machine learning classifiers, there are no complex b...
research
∙
10/31/2022
Class Interference of Deep Neural Networks
Recognizing and telling similar objects apart is even hard for human bei...
research
∙
05/20/2022
Sigmoidally Preconditioned Off-policy Learning:a new exploration method for reinforcement learning
One of the major difficulties of reinforcement learning is learning from...
research
∙
04/01/2022
Learning to Accelerate by the Methods of Step-size Planning
Gradient descent is slow to converge for ill-conditioned problems and no...
research
∙
12/21/2021
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
Autonomous driving has achieved a significant milestone in research and ...
research
∙
11/20/2021
Towards safe, explainable, and regulated autonomous driving
There has been growing interest in the development and deployment of aut...
research
∙
09/17/2021
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations
In real scenarios, state observations that an agent observes may contain...
research
∙
01/21/2021
Breaking the Deadly Triad with a Target Network
The deadly triad refers to the instability of a reinforcement learning a...
research
∙
09/14/2020
Variance-Reduced Off-Policy Memory-Efficient Policy Search
Off-policy policy optimization is a challenging problem in reinforcement...
research
∙
07/19/2020
Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities
Model-based reinforcement learning (MBRL) can significantly improve samp...
research
∙
07/07/2020
Towards a practical measure of interference for reinforcement learning
Catastrophic interference is common in many network-based learning syste...
research
∙
01/26/2020
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Inputs
Significant progress has been made recently in developing few-shot objec...
research
∙
12/18/2019
One-Shot Weakly Supervised Video Object Segmentation
Conventional few-shot object segmentation methods learn object segmentat...
research
∙
11/11/2019
Provably Convergent Off-Policy Actor-Critic with Function Approximation
We present the first provably convergent off-policy actor-critic algorit...
research
∙
11/08/2019
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans
We propose a method to tackle the problem of mapless collision-avoidance...
research
∙
10/03/2019
Is Fast Adaptation All You Need?
Gradient-based meta-learning has proven to be highly effective at learni...
research
∙
06/18/2019
Hill Climbing on Value Estimates for Search-control in Dyna
Dyna is an architecture for model-based reinforcement learning (RL), whe...
research
∙
05/13/2019
Distributional Reinforcement Learning for Efficient Exploration
In distributional reinforcement learning (RL), the estimated distributio...
research
∙
03/20/2019
Reinforcing Classical Planning for Adversary Driving Scenarios
Adversary scenarios in driving, where the other vehicles may make mistak...
research
∙
03/18/2019
Deep Reinforcement Learning with Decorrelation
Learning an effective representation for high-dimensional data is a chal...
research
∙
11/06/2018
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search
In this paper, we propose an actor ensemble algorithm, named ACE, for co...
research
∙
11/05/2018
QUOTA: The Quantile Option Architecture for Reinforcement Learning
In this paper, we propose the Quantile Option Architecture (QUOTA) for e...
research
∙
04/27/2018
Negative Log Likelihood Ratio Loss for Deep Neural Network Classification
In deep neural network, the cross-entropy loss function is commonly used...
research
∙
02/08/2018