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Harry Zhao

PhD - McGill University
Supervisor
Doina Precup
Co-supervisor
Yoshua Bengio
Research Topics
Computational Neuroscience
Reasoning
Reinforcement Learning
Representation Learning
Website
Google Scholar
GitHub

Publications

Rejecting Hallucinated State Targets during Planning
Harry Zhao
Mingde Zhao
Tristan Sylvain
Romain Laroche
Doina Precup
Yoshua Bengio
2025-05-01
ICML.cc/2025/Conference (poster)
openreview.net
openreview.net
Identifying and Addressing Delusions for Target-Directed Decision-Making
Harry Zhao
Mingde Zhao
Tristan Sylvain
Romain Laroche
Doina Precup
Yoshua Bengio
We are interested in target-directed agents, which produce targets during decision-time planning, to guide their behaviors and achieve bette… (see more)r generalization during evaluation. Improper training of these agents can result in delusions: the agent may come to hold false beliefs about the targets, which cannot be properly rejected, leading to unwanted behaviors and damaging out-of-distribution generalization. We identify different types of delusions by using intuitive examples in carefully controlled environments, and investigate their causes. We demonstrate how delusions can be addressed for agents trained by hindsight relabeling, a mainstream approach in for training target-directed RL agents. We validate empirically the effectiveness of the proposed solutions in correcting delusional behaviors and improving out-of-distribution generalization.
2024-10-12
NeurIPS.cc/2024/Workshop/SafeGenAi (poster)
doi.org
openreview.net
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Harry Zhao 0001
Harry Zhao
Mingde Zhao
Safa Alver
Harm van Seijen
Romain Laroche
Doina Precup
Yoshua Bengio
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstracti… (see more)ons to generalize better in novel situations. It automatically decomposes the given task into smaller, more manageable subtasks, and thus enables sparse decision-making and focused computation on the relevant parts of the environment. The decomposition relies on the extraction of an abstracted proxy problem represented as a directed graph, in which vertices and edges are learned end-to-end from hindsight. Our theoretical analyses provide performance guarantees under appropriate assumptions and establish where our approach is expected to be helpful. Generalization-focused experiments validate Skipper’s significant advantage in zero-shot generalization, compared to some existing state-of-the-art hierarchical planning methods.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
Sitao Luan
Harry Zhao
Mingde Zhao
Chenqing Hua
Xiao-Wen Chang
Doina Precup
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filte… (see more)rs the neighborhood node information. Though effective for various tasks, in this paper, we show that they are potentially a problematic factor underlying all GNN methods for learning on certain datasets, as they force the node representations similar, making the nodes gradually lose their identity and become indistinguishable. Hence, we augment the aggregation operations with their dual, i.e. diversification operators that make the node more distinct and preserve the identity. Such augmentation replaces the aggregation with a two-channel filtering process that, in theory, is beneficial for enriching the node representations. In practice, the proposed two-channel filters can be easily patched on existing GNN methods with diverse training strategies, including spectral and spatial (message passing) methods. In the experiments, we observe desired characteristics of the models and significant performance boost upon the baselines on 9 node classification tasks.
2022-11-22
NeurIPS.cc/2022/Workshop/GLFrontiers (published)
doi.org
openreview.net
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
Akram Erraqabi
Marlos C. Machado
Harry Zhao
Mingde Zhao
Sainbayar Sukhbaatar
Alessandro Lazaric
Ludovic Denoyer
Yoshua Bengio
In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from… (see more) skill discovery to reward shaping. Recently, learning the Laplacian representation has been framed as the optimization of a temporally-contrastive objective to overcome its computational limitations in large (or continuous) state spaces. However, this approach requires uniform access to all states in the state space, overlooking the exploration problem that emerges during the representation learning process. In this work, we propose an alternative method that is able to recover, in a non-uniform-prior setting, the expressiveness and the desired properties of the Laplacian representation. We do so by combining the representation learning with a skill-based covering policy, which provides a better training distribution to extend and refine the representation. We also show that a simple augmentation of the representation objective with the learned temporal abstractions improves dynamics-awareness and helps exploration. We find that our method succeeds as an alternative to the Laplacian in the non-uniform setting and scales to challenging continuous control environments. Finally, even if our method is not optimized for skill discovery, the learned skills can successfully solve difficult continuous navigation tasks with sparse rewards, where standard skill discovery approaches are no so effective.
2022-05-20
auai.org/UAI/2022/Conference (poster)
doi.org
openreview.net
Revisiting Heterophily For Graph Neural Networks
Sitao Luan
Chenqing Hua
Qincheng Lu
Jiaqi Zhu
Harry Zhao
Mingde Zhao
Shuyuan Zhang
Xiao-Wen Chang
Doina Precup
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily … (see more)assumption). While GNNs have been commonly believed to outperform NNs in real-world tasks, recent work has identified a non-trivial set of datasets where their performance compared to NNs is not satisfactory. Heterophily has been considered the main cause of this empirical observation and numerous works have been put forward to address it. In this paper, we first revisit the widely used homophily metrics and point out that their consideration of only graph-label consistency is a shortcoming. Then, we study heterophily from the perspective of post-aggregation node similarity and define new homophily metrics, which are potentially advantageous compared to existing ones. Based on this investigation, we prove that some harmful cases of heterophily can be effectively addressed by local diversification operation. Then, we propose the Adaptive Channel Mixing (ACM), a framework to adaptively exploit aggregation, diversification and identity channels node-wisely to extract richer localized information for diverse node heterophily situations. ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers. When evaluated on 10 benchmark node classification tasks, ACM-augmented baselines consistently achieve significant performance gain, exceeding state-of-the-art GNNs on most tasks without incurring significant computational burden.
openreview.net
Exploration-Driven Representation Learning in Reinforcement Learning
Akram Erraqabi
Harry Zhao
Mingde Zhao
Marlos C. Machado
Yoshua Bengio
Sainbayar Sukhbaatar
Ludovic Denoyer
Alessandro Lazaric
Learning reward-agnostic representations is an emerging paradigm in reinforcement learning. These representations can be leveraged for sever… (see more)al purposes ranging from reward shaping to skill discovery. Nevertheless, in order to learn such representations, existing methods often rely on assuming uniform access to the state space. With such a privilege, the agent’s coverage of the environment can be limited which hurts the quality of the learned representations. In this work, we introduce a method that explicitly couples representation learning with exploration when the agent is not provided with a uniform prior over the state space. Our method learns representations that constantly drive exploration while the data generated by the agent’s exploratory behavior drives the learning of better representations. We empirically validate our approach in goal-achieving tasks, demonstrating that the learned representation captures the dynamics of the environment, leads to more accurate value estimation, and to faster credit assignment, both when used for control and for reward shaping. Finally, the exploratory policy that emerges from our approach proves to be successful at continuous navigation tasks with sparse rewards.
2021-07-22
ICML.cc/2021/Workshop/URL (poster)
openreview.net
openreview.net
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
Harry Zhao
Mingde Zhao
Zhen Liu
Sitao Luan
Shuyuan Zhang
Doina Precup
Yoshua Bengio
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during plan… (see more)ning. The agent uses a bottleneck mechanism over a set-based representation to force the number of entities to which the agent attends at each planning step to be small. In experiments, we investigate the bottleneck mechanism with several sets of customized environments featuring different challenges. We consistently observe that the design allows the planning agents to generalize their learned task-solving abilities in compatible unseen environments by attending to the relevant objects, leading to better out-of-distribution generalization performance.
openreview.net