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Found 24 papers, 5 papers with code
Control-Aware Representations for Model-based Reinforcement Learning
We first formulate a LCE model to learn representations that are suitable to be used by a policy iteration style algorithm in the latent space.
Model-based Reinforcement Learning
reinforcement-learning
+2
Non-Stationary Off-Policy Optimization
This approach is practical and analyzable, and we provide guarantees on both the quality of off-policy optimization and the regret during online deployment.
Latent Bandits Revisited
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state.
Variational Model-based Policy Optimization
Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL.
Predictive Coding for Locally-Linear Control
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.
BRPO: Batch Residual Policy Optimization
In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e. g., by constraining the learned action distribution to differ from the behavior policy by some maximum degree that is the same at each state.
AlgaeDICE: Policy Gradient from Arbitrary Experience
In many real-world applications of reinforcement learning (RL), interactions with the environment are limited due to cost or feasibility.
CAQL: Continuous Action Q-Learning
Value-based reinforcement learning (RL) methods like Q-learning have shown success in a variety of domains.
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space.
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
In contrast to previous approaches, our algorithm is agnostic to knowledge of the behavior policy (or policies) used to generate the dataset.
Lyapunov-based Safe Policy Optimization for Continuous Control
We formulate these problems as constrained Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them.
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare.
Risk-Sensitive Generative Adversarial Imitation Learning
We then derive two different versions of our RS-GAIL optimization problem that aim at matching the risk profiles of the agent and the expert w. r. t.
A Lyapunov-based Approach to Safe Reinforcement Learning
In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints.
More Robust Doubly Robust Off-policy Evaluation
In particular, we focus on the doubly robust (DR) estimators that consist of an importance sampling (IS) component and a performance model, and utilize the low (or zero) bias of IS and low variance of the model at the same time.
Path Consistency Learning in Tsallis Entropy Regularized MDPs
In this paper, we follow the work of Nachum et al. (2017) in the soft ERL setting, and propose a class of novel path consistency learning (PCL) algorithms, called {\em sparse PCL}, for the sparse ERL problem that can work with both on-policy and off-policy data.
Imitation Learning from Visual Data with Multiple Intentions
In this paper we present an LfD approach for learning multiple modes of behavior from visual data.
Safe Policy Improvement by Minimizing Robust Baseline Regret
We show that our formulation is NP-hard and propose an approximate algorithm.
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account \emph{risk}, i. e., increased awareness of events of small probability and high consequences.
Two Phase $Q-$learning for Bidding-based Vehicle Sharing
We consider one-way vehicle sharing systems where customers can rent a car at one station and drop it off at another.
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
Our first contribution is to show that a CVaR objective, besides capturing risk sensitivity, has an alternative interpretation as expected cost under worst-case modeling errors, for a given error budget.
Policy Gradient for Coherent Risk Measures
For static risk measures, our approach is in the spirit of policy gradient algorithms and combines a standard sampling approach with convex programming.
Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning
We aim to bridge the gap between these two methods in solving constrained overdetermined linear regression problems---e. g., $\ell_2$ and $\ell_1$ regression problems.
Algorithms for CVaR Optimization in MDPs
In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in costs in addition to minimizing a standard criterion.
