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Soft-IntroVAE: 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 Selfish-Mining in Blockchains
A proof of work (PoW) blockchain protocol distributes rewards to its par...
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Hallucinative Topological Memory for Zero-Shot Visual Planning
In visual planning (VP), an agent learns to plan goal-directed behavior ...
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Sub-Goal Trees – a Framework for Goal-Based Reinforcement Learning
Many AI problems, in robotics and other domains, are goal-based, essenti...
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Deep Residual Flow for Novelty Detection
The effective application of neural networks in the real-world relies on...
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Deep Variational Semi-Supervised 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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Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization
Many AI problems, in robotics and other domains, are goal-directed, 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 High-Precision Robotic Assembly
Precise robotic manipulation skills are desirable in many industrial set...
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Multi Agent Reinforcement Learning with Multi-Step 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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Model-Ensemble Trust-Region Policy Optimization
Model-free 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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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
We explore deep reinforcement learning methods for multi-agent domains. ...
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Shallow Updates for Deep Reinforcement Learning
Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQ...
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Situational Awareness by Risk-Conscious 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: Bias-Variance Analysis
We consider the off-policy 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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Risk-Sensitive and Robust Decision-Making: 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 risk-sensitive 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 large-scale 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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