
Training a FirstOrder Theorem Prover from Synthetic Data
A major challenge in applying machine learning to automated theorem prov...
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Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
A major challenge in reinforcement learning is the design of exploration...
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Towards Continual Reinforcement Learning: A Review and Perspectives
In this article, we aim to provide a literature review of different form...
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Gradient Starvation: A Learning Proclivity in Neural Networks
We identify and formalize a fundamental gradient descent phenomenon resu...
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DiversityEnriched OptionCritic
Temporal abstraction allows reinforcement learning agents to represent k...
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A Study of Policy Gradient on a Class of Exactly Solvable Models
Policy gradient methods are extensively used in reinforcement learning a...
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Forethought and Hindsight in Credit Assignment
We address the problem of credit assignment in reinforcement learning an...
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Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning
In this paper, we present connections between three models used in diffe...
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A Fully Tensorized Recurrent Neural Network
Recurrent neural networks (RNNs) are powerful tools for sequential model...
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Reward Propagation Using Graph Convolutional Networks
Potentialbased reward shaping provides an approach for designing good r...
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Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
The core operation of Graph Neural Networks (GNNs) is the aggregation en...
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Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
The performance limit of Graph Convolutional Networks (GCNs) and the fac...
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An Equivalence between Loss Functions and NonUniform Sampling in Experience Replay
Prioritized Experience Replay (PER) is a deep reinforcement learning tec...
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What can I do here? A Theory of Affordances in Reinforcement Learning
Reinforcement learning algorithms usually assume that all actions are al...
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Learning to Prove from Synthetic Theorems
A major challenge in applying machine learning to automated theorem prov...
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A Brief Look at Generalization in Visual MetaReinforcement Learning
Due to the realization that deep reinforcement learning algorithms train...
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Learning to cooperate: Emergent communication in multiagent navigation
Emergent communication in artificial agents has been studied to understa...
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A Distributional Analysis of SamplingBased Reinforcement Learning Algorithms
We present a distributional approach to theoretical analyses of reinforc...
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Interference and Generalization in Temporal Difference Learning
We study the link between generalization and interference in temporaldi...
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Invariant Causal Prediction for Block MDPs
Generalization across environments is critical to the successful applica...
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Policy Evaluation Networks
Many reinforcement learning algorithms use value functions to guide the ...
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oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions
Explicit engineering of reward functions for given environments has been...
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Valuedriven Hindsight Modelling
Value estimation is a critical component of the reinforcement learning (...
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Provably efficient reconstruction of policy networks
Recent research has shown that learning policies parametrized by large ...
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Options of Interest: Temporal Abstraction with Interest Functions
Temporal abstraction refers to the ability of an agent to use behaviours...
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Shaping representations through communication: community size effect in artificial learning systems
Motivated by theories of language and communication that explain why com...
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Marginalized State Distribution Entropy Regularization in Policy Optimization
Entropy regularization is used to get improved optimization performance ...
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Doubly Robust OffPolicy ActorCritic Algorithms for Reinforcement Learning
We study the problem of offpolicy critic evaluation in several variants...
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Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods
The policy gradient theorem is defined based on an objective with respec...
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Hindsight Credit Assignment
We consider the problem of efficient credit assignment in reinforcement ...
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Optioncritic in cooperative multiagent systems
In this paper, we investigate learning temporal abstractions in cooperat...
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Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Textbased games are a natural challenge domain for deep reinforcement l...
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Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
Learning and planning in partiallyobservable domains is one of the most...
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Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
Millions of blind and visuallyimpaired (BVI) people navigate urban envi...
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Actor Critic with Differentially Private Critic
Reinforcement learning algorithms are known to be sample inefficient, an...
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Augmenting learning using symmetry in a biologicallyinspired domain
Invariances to translation, rotation and other spatial transformations a...
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Avoidance Learning Using Observational Reinforcement Learning
Imitation learning seeks to learn an expert policy from sampled demonstr...
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Revisit Policy Optimization in Matrix Form
In tabular case, when the reward and environment dynamics are known, pol...
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An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Batch normalization has been widely used to improve optimization in deep...
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Selfsupervised Learning of Distance Functions for GoalConditioned Reinforcement Learning
Goalconditioned policies are used in order to break down complex reinfo...
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Neural Transfer Learning for Crybased Diagnosis of Perinatal Asphyxia
Despite continuing medical advances, the rate of newborn morbidity and m...
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SVRG for Policy Evaluation with Fewer Gradient Evaluations
Stochastic variancereduced gradient (SVRG) is an optimization method or...
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Break the Ceiling: Stronger Multiscale Deep Graph Convolutional Networks
Recently, neural network based approaches have achieved significant impr...
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Recurrent Value Functions
Despite recent successes in Reinforcement Learning, valuebased methods ...
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Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Diversity of environments is a key challenge that causes learned robotic...
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Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials
The stochastic multiarmed bandit problem is a wellknown model for stud...
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The Termination Critic
In this work, we consider the problem of autonomously discovering behavi...
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ClusteringOriented Representation Learning with AttractiveRepulsive Loss
The standard loss function used to train neural network classifiers, cat...
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OffPolicy Deep Reinforcement Learning without Exploration
Reinforcement learning traditionally considers the task of balancing exp...
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Environments for Lifelong Reinforcement Learning
To achieve general artificial intelligence, reinforcement learning (RL) ...
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Doina Precup
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Associate Professor School of Computer Science at McGill University