23 citations · 77 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…
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…
Proceedings 35th International Conference on Logic Programming (Technical Communications)
Bart Bogaerts, Esra Erdem, Paul Fodor +7
Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are sought in all ar…
A Joint Planning and Learning Framework for Human-Aided Decision-Making
Daoming Lyu, Fangkai Yang, Bo Liu +1
Conventional reinforcement learning (RL) allows an agent to learn policies via environmental rewards only, with a long and slow learning curve, especially at the beginning stage. O…
Knowledge-Based Sequential Decision-Making Under Uncertainty
Daoming Lyu
Deep reinforcement learning (DRL) algorithms have achieved great success on sequential decision-making problems, yet is criticized for the lack of data-efficiency and explainabilit…
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…
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
Bo Liu, Tengyang Xie, Yangyang Xu +4
Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare. The mean-variance function is…
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…