Search Results for author:
Found 40 papers, 21 papers with code
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits
In this paper, we study exposure bias in a class of well-known contextual bandit algorithms known as Linear Cascading Bandits.
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
Debiasing methods aim to mitigate the effect of selection bias on the evaluation and optimization of RSs.
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
Conventional reinforcement learning (RL) methods can successfully solve a wide range of sequential decision problems.
Hierarchical Reinforcement Learning for Power Network Topology Control
Whereas at the highest level a purely rule-based policy is still chosen for all agents in this study, at the intermediate level the policy is trained using different state-of-the-art RL algorithms.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes
In this work, we propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem with an Attentive Conditional Neural Process model.
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
A natural solution concept in such settings is the Stackelberg equilibrium, in which the ``leader'' agent selects the strategy that maximizes its own payoff given that the ``follower'' agent will choose their best response to this strategy.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
Reusable Options through Gradient-based Meta Learning
Subsequently, we formulate the desiderata for reusable options and use these to frame the problem of learning options as a gradient-based meta-learning problem.
Exposure-Aware Recommendation using Contextual Bandits
This is especially problematic when bias is amplified over time as a few items (e. g., popular ones) are repeatedly over-represented in recommendation lists and users' interactions with those items will amplify bias towards those items over time resulting in a feedback loop.
Calculus on MDPs: Potential Shaping as a Gradient
In reinforcement learning, different reward functions can be equivalent in terms of the optimal policies they induce.
Neural Topological Ordering for Computation Graphs
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine
Hex is a turn-based two-player connection game with a high branching factor, making the game arbitrarily complex with increasing board sizes.
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation
We present Nonparametric Approximation of Inter-Trace returns (NAIT), a Reinforcement Learning algorithm for discrete action, pixel-based environments that is both highly sample and computation efficient.
Ranked #14 on
Atari Games 100k
on Atari 100k
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment
We consider multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods
Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains.
Multi-Agent MDP Homomorphic Networks
This paper introduces Multi-Agent MDP Homomorphic Networks, a class of networks that allows distributed execution using only local information, yet is able to share experience between global symmetries in the joint state-action space of cooperative multi-agent systems.
Value Refinement Network (VRN)
Combining the benefits of planning and learning values, we propose the Value Refinement Network (VRN), an architecture that locally refines a plan in a (simpler) state space abstraction, represented by a pre-computed value function, with respect to the full agent state.
Hierarchies of Planning and Reinforcement Learning for Robot Navigation
In simulated robotic navigation tasks, VI-RL results in consistent strong improvement over vanilla RL, is on par with vanilla hierarchal RL on single layouts but more broadly applicable to multiple layouts, and is on par with trainable HL path planning baselines except for a parking task with difficult non-holonomic dynamics where it shows marked improvements.
A Survey of Exploration Methods in Reinforcement Learning
Exploration is an essential component of reinforcement learning algorithms, where agents need to learn how to predict and control unknown and often stochastic environments.
Meta Reinforcement Learning for Fast Adaptation of Hierarchical Policies
Hierarchical methods have the potential to allow reinforcement learning to scale to larger environments.
Combining Reward Information from Multiple Sources
We study this problem in the setting with two conflicting reward functions learned from different sources.
Deep Policy Dynamic Programming for Vehicle Routing Problems
Routing problems are a class of combinatorial problems with many practical applications.
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models
Reinforcement learning is a promising paradigm for solving sequential decision-making problems, but low data efficiency and weak generalization across tasks are bottlenecks in real-world applications.
Deep Coherent Exploration For Continuous Control
In policy search methods for reinforcement learning (RL), exploration is often performed by injecting noise either in action space at each step independently or in parameter space over each full trajectory.
Experimental design for MRI by greedy policy search
In today's clinical practice, magnetic resonance imaging (MRI) is routinely accelerated through subsampling of the associated Fourier domain.
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
Neural processes (NPs) constitute a family of variational approximate models for stochastic processes with promising properties in computational efficiency and uncertainty quantification.
An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques
Air traffic control is becoming a more and more complex task due to the increasing number of aircraft.
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
MDP homomorphic networks are neural networks that are equivariant under symmetries in the joint state-action space of an MDP.
Social Navigation with Human Empowerment driven Deep Reinforcement Learning
In contrast to self-empowerment, a robot employing our approach strives for the empowerment of people in its environment, so they are not disturbed by the robot's presence and motion.
Estimating Gradients for Discrete Random Variables by Sampling without Replacement
We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples.
Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales
PAC Bayes is a generalized framework which is more resistant to overfitting and that yields performance bounds that hold with arbitrarily high probability even on the unjustified extrapolations.
Reinforcement Learning with Non-uniform State Representations for Adaptive Search
In this paper, we present a search algorithm that generates efficient trajectories that optimize the rate at which probability mass is covered by a searcher.
Buy 4 REINFORCE Samples, Get a Baseline for Free!
REINFORCE can be used to train models in structured prediction settings to directly optimize the test-time objective.
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
We show how to implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Search.
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Learned controllers such as neural networks typically do not have a notion of uncertainty that allows to diagnose an offset between training and testing conditions, and potentially intervene.
Deep Generative Modeling of LiDAR Data
In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map.
BanditSum: Extractive Summarization as a Contextual Bandit
In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels.
Ranked #10 on
Extractive Text Summarization
on CNN / Daily Mail
Attention, Learn to Solve Routing Problems!
The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development.
Addressing Function Approximation Error in Actor-Critic Methods
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.
Ranked #2 on
OpenAI Gym
on Ant-v4
An inference-based policy gradient method for learning options
In this work we develop a novel policy gradient method for the automatic learning of policies with options.
Policy Search with High-Dimensional Context Variables
A naive application of unsupervised dimensionality reduction methods to the context variables, such as principal component analysis, is insufficient as task-relevant input may be ignored.
