
SoftIntroVAE: Analyzing and Improving the Introspective Variational Autoencoder
The recently introduced introspective variational autoencoder (IntroVAE)...
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Online Safety Assurance for Deep Reinforcement Learning
Recently, deep learning has been successfully applied to a variety of ne...
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Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores
To compute robust 2D assembly plans, we present an approach that combine...
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Offline Meta Reinforcement Learning
Consider the following problem, which we term Offline Meta Reinforcement...
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Efficient MDP Analysis for SelfishMining in Blockchains
A proof of work (PoW) blockchain protocol distributes rewards to its par...
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Hallucinative Topological Memory for ZeroShot Visual Planning
In visual planning (VP), an agent learns to plan goaldirected behavior ...
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SubGoal Trees – a Framework for GoalBased Reinforcement Learning
Many AI problems, in robotics and other domains, are goalbased, essenti...
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Deep Residual Flow for Novelty Detection
The effective application of neural networks in the realworld relies on...
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Deep Variational SemiSupervised Novelty Detection
In anomaly detection (AD), one seeks to identify whether a test sample i...
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Bayesian Relational Memory for Semantic Visual Navigation
We introduce a new memory architecture, Bayesian Relational Memory (BRM)...
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SubGoal Trees  a Framework for GoalDirected Trajectory Prediction and Optimization
Many AI problems, in robotics and other domains, are goaldirected, esse...
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Harnessing Reinforcement Learning for Neural Motion Planning
Motion planning is an essential component in most of today's robotic app...
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Learning Robotic Manipulation through Visual Planning and Acting
Planning for robotic manipulation requires reasoning about the changes a...
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Domain Randomization for Active Pose Estimation
Accurate state estimation is a fundamental component of robotic control....
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Reinforcement Learning on Variable Impedance Controller for HighPrecision Robotic Assembly
Precise robotic manipulation skills are desirable in many industrial set...
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Multi Agent Reinforcement Learning with MultiStep Generative Models
The dynamics between agents and the environment are an important compone...
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Internet Congestion Control via Deep Reinforcement Learning
We present and investigate a novel and timely application domain for dee...
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Learning and Planning with a Semantic Model
Building deep reinforcement learning agents that can generalize and adap...
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Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
The recently proposed distributional approach to reinforcement learning ...
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Learning Plannable Representations with Causal InfoGAN
In recent years, deep generative models have been shown to 'imagine' con...
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Safe Policy Learning from Observations
In this paper, we consider the problem of learning a policy by observing...
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Learning Robotic Assembly from CAD
In this work, motivated by recent manufacturing trends, we investigate a...
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ModelEnsemble TrustRegion Policy Optimization
Modelfree reinforcement learning (RL) methods are succeeding in a growi...
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Safer Classification by Synthesis
The discriminative approach to classification using deep neural networks...
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Situationally Aware Options
Hierarchical abstractions, also known as options  a type of temporally...
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Learning Generalized Reactive Policies using Deep Neural Networks
We consider the problem of learning for planning, where knowledge acquir...
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MultiAgent ActorCritic for Mixed CooperativeCompetitive Environments
We explore deep reinforcement learning methods for multiagent domains. ...
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Shallow Updates for Deep Reinforcement Learning
Deep reinforcement learning (DRL) methods such as the Deep QNetwork (DQ...
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Situational Awareness by RiskConscious Skills
Hierarchical Reinforcement Learning has been previously shown to speed u...
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Learning from the Hindsight Plan  Episodic MPC Improvement
Model predictive control (MPC) is a popular control method that has prov...
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Bayesian Reinforcement Learning: A Survey
Bayesian methods for machine learning have been widely investigated, yie...
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Value Iteration Networks
We introduce the value iteration network (VIN): a fully differentiable n...
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Generalized Emphatic Temporal Difference Learning: BiasVariance Analysis
We consider the offpolicy evaluation problem in Markov decision process...
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Emphatic TD Bellman Operator is a Contraction
Recently, SuttonMW15 introduced the emphatic temporal differences (ETD) ...
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RiskSensitive and Robust DecisionMaking: a CVaR Optimization Approach
In this paper we address the problem of decision making within a Markov ...
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Policy Gradient for Coherent Risk Measures
Several authors have recently developed risksensitive policy gradient m...
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Implicit Temporal Differences
In reinforcement learning, the TD(λ) algorithm is a fundamental policy e...
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Optimizing the CVaR via Sampling
Conditional Value at Risk (CVaR) is a prominent risk measure that is bei...
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Scaling Up Robust MDPs by Reinforcement Learning
We consider largescale Markov decision processes (MDPs) with parameter ...
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Policy Evaluation with Variance Related Risk Criteria in Markov Decision Processes
In this paper we extend temporal difference policy evaluation algorithms...
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Policy Gradients with Variance Related Risk Criteria
Managing risk in dynamic decision problems is of cardinal importance in ...
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