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Found 23 papers, 5 papers with code
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
Better state exploration using action sequence equivalence
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL).
Low-Rank Projections of GCNs Laplacian
In this work, we study the behavior of standard models for community detection under spectral manipulations.
Interferometric Graph Transform for Community Labeling
We present a new approach for learning unsupervised node representations in community graphs.
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize.
Relevant Action Matters : Motivating agent with action usefulness
We evaluate RAM on the procedurally-generated environment MiniGrid, against state-of-the-art methods.
Adversarially Guided Actor-Critic
Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tasks where efficient exploration is a bottleneck.
A spectral perspective on GCNs
In this work, we study the behavior of standard GCNs under spectral manipulations.
Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization.
Learning Value Functions in Deep Policy Gradients using Residual Variance
We prove the theoretical consistency of the new gradient estimator and observe dramatic empirical improvement across a variety of continuous control tasks and algorithms.
A Machine of Few Words -- Interactive Speaker Recognition with Reinforcement Learning
To do so, we cast the speaker recognition task into a sequential decision-making problem that we solve with Reinforcement Learning.
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action
Violating constraints thus results in rejected actions or entering in a safe mode driven by an external controller, making RL agents incapable of learning from their mistakes.
MERL: Multi-Head Reinforcement Learning
In this paper: (a) We introduce and define MERL, the multi-head reinforcement learning framework we use throughout this work.
Samples Are Useful? Not Always: denoising policy gradient updates using variance explained
In this work, Vex is used to evaluate the impact each transition will have on learning: this criterion refines sampling and improves the policy gradient algorithm.
Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL
In this work, we use this metric to select samples that are useful to learn from, and we demonstrate that this selection can significantly improve the performance of policy gradient methods.
Visual Reasoning with Multi-hop Feature Modulation
Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue.
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation
Recommender systems objectives can be broadly characterized as modeling user preferences over short-or long-term time horizon.
Operator-valued Kernels for Learning from Functional Response Data
In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function.
A Generative Model of Software Dependency Graphs to Better Understand Software Evolution
Then, we propose a generative model of software dependency graphs which synthesizes graphs whose degree distribution is close to the empirical ones observed in real software systems.
Software Engineering
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques
In general the evaluation of a RS is a critical issue.
Multiple Operator-valued Kernel Learning
We study this problem in the case of kernel ridge regression for functional responses with an lr-norm constraint on the combination coefficients.
A Generalized Kernel Approach to Structured Output Learning
Finally, we evaluate the performance of our KDE approach using both covariance and conditional covariance kernels on two structured output problems, and compare it to the state-of-the-art kernel-based structured output regression methods.
