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Hengshuai Yao

7 accepted papers

2023

The Sufficiency of Off-Policyness and Soft Clipping: PPO Is Still Insufficient according to an Off-Policy Measure

AAAI 2023technical

The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space. Does there exist better policies outside of this space? By using a novel surrogate objective that employs the sigmoid function (which provides an interesting way of exploration), we found th…

2022

Understanding and mitigating the limitations of prioritized experience replay

UAI 2022poster

Prioritized Experience Replay (ER) has been empirically shown to improve sample efficiency across many domains and attracted great attention; however, there is little theoretical understanding of why such prioritized sampling helps and its limitations. In this work, we take a deep look at the priori…

Cited by 25SourcePDFScholar
2020

Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans

ICRA 2020poster

We consider the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from the robot's perspective and social-safety to measure the impact of robot's actions on surrounding pedestrians. Specifically,…

Cited by 81SourceScholar
2020

Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation

ICML 2020poster

We present the first provably convergent two-timescale off-policy actor-critic algorithm (COF-PAC) with function approximation. Key to COF-PAC is the introduction of a new critic, the emphasis critic, which is trained via Gradient Emphasis Learning (GEM), a novel combination of the key ideas of Grad…

2020

Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings

IJCAI 2020poster

Significant progress has been made recently in developing few-shot object segmentation methods. Learning is shown to be successful in few-shot segmentation settings, using pixel-level, scribbles and bounding box supervision. This paper takes another approach, i.e., only requiring image-level label f…

Cited by 0SourcePDFScholar
2019

Distributional Reinforcement Learning for Efficient Exploration

ICML 2019oral

In distributional reinforcement learning (RL), the estimated distribution of value functions model both the parametric and intrinsic uncertainties. We propose a novel and efficient exploration method for deep RL that has two components. The first is a decaying schedule to suppress the intrinsic unce…

Cited by 112SourcePDFScholar