22 citations · 74 across the 13 of their papers we have counts for
13 papers
PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agent
Daoming Lyu, Bo Liu, Jianshu Chen
We consider the problem of multi-task reasoning (MTR), where an agent can solve multiple tasks via (first-order) logic reasoning. This capability is essential for human-like intell…
STOPS: Short-Term-based Volatility-controlled Policy Search and its Global Convergence
Liangliang Xu, Daoming Lyu, Yangchen Pan +2
It remains challenging to deploy existing risk-averse approaches to real-world applications. The reasons are multi-fold, including the lack of global optimality guarantee and the n…
TDM: Trustworthy Decision-Making via Interpretability Enhancement
Daoming Lyu, Fangkai Yang, Hugh Kwon +3
Human-robot interactive decision-making is increasingly becoming ubiquitous, and trust is an influential factor in determining the reliance on autonomy. However, it is not reasonab…
Variance-Reduced Off-Policy Memory-Efficient Policy Search
Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh +3
Off-policy policy optimization is a challenging problem in reinforcement learning (RL). The algorithms designed for this problem often suffer from high variance in their estimators…
Stable and Efficient Policy Evaluation
Daoming Lyu, Bo Liu, Matthieu Geist +3
Policy evaluation algorithms are essential to reinforcement learning due to their ability to predict the performance of a policy. However, there are two long-standing issues lying…
Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning
Shangtong Zhang, Bo Liu, Shimon Whiteson
We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a discounted infinite horizon MDP optimizing the variance of a per-step reward random variab…
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
Shangtong Zhang, Bo Liu, Shimon Whiteson
We present GradientDICE for estimating the density ratio between the state distribution of the target policy and the sampling distribution in off-policy reinforcement learning. Gra…
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
Shangtong Zhang, Bo Liu, Hengshuai Yao +1
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,…
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming
Daoming Lyu, Fangkai Yang, Bo Liu +1
Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry. By contrast, human l…
QUOTA: The Quantile Option Architecture for Reinforcement Learning
Shangtong Zhang, Borislav Mavrin, Linglong Kong +2
In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL). In QUOTA, decision making…
SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning
Daoming Lyu, Fangkai Yang, Bo Liu +1
Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretab…
PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making
Fangkai Yang, Daoming Lyu, Bo Liu +1
Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world…
OTD: (Near)-Optimal Off-Policy TD Learning
Bo Liu, Daoming Lyu, Wen Dong +1
Temporal difference learning and Residual Gradient methods are the most widely used temporal difference based learning algorithms; however, it has been shown that none of their obj…