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Found 18 papers, 6 papers with code
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy
Our metric, the $\lambda$-discrepancy, is the difference between two distinct temporal difference (TD) value estimates, each computed using TD($\lambda$) with a different value of $\lambda$.
Decision-Focused Model-based Reinforcement Learning for Reward Transfer
We also provide theoretical and empirical evidence, on a variety of simulators and real patient data, that RDF can learn simple yet effective models that can be used to plan personalized policies.
On the Geometry of Reinforcement Learning in Continuous State and Action Spaces
Central to our work is the idea that the transition dynamics induce a low dimensional manifold of reachable states embedded in the high-dimensional nominal state space.
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes
In the reinforcement learning literature, there are many algorithms developed for either Contextual Bandit (CB) or Markov Decision Processes (MDP) environments.
Faster Deep Reinforcement Learning with Slower Online Network
In this paper we endow two popular deep reinforcement learning algorithms, namely DQN and Rainbow, with updates that incentivize the online network to remain in the proximity of the target network.
Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation
Off-policy policy evaluation methods for sequential decision making can be used to help identify if a proposed decision policy is better than a current baseline policy.
Coarse-Grained Smoothness for RL in Metric Spaces
We propose a new coarse-grained smoothness definition that generalizes the notion of Lipschitz continuity, is more widely applicable, and allows us to compute significantly tighter bounds on Q-functions, leading to improved learning.
State Relevance for Off-Policy Evaluation
Importance sampling-based estimators for off-policy evaluation (OPE) are valued for their simplicity, unbiasedness, and reliance on relatively few assumptions.
Learning Markov State Abstractions for Deep Reinforcement Learning
A fundamental assumption of reinforcement learning in Markov decision processes (MDPs) is that the relevant decision process is, in fact, Markov.
Learning to search efficiently for causally near-optimal treatments
Finding an effective medical treatment often requires a search by trial and error.
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deployment in high stakes settings requires ways of assessing its validity.
A general method for regularizing tensor decomposition methods via pseudo-data
Tensor decomposition methods allow us to learn the parameters of latent variable models through decomposition of low-order moments of data.
Combining Parametric and Nonparametric Models for Off-Policy Evaluation
We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning.
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown.
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare.
Representation Balancing MDPs for Off-Policy Policy Evaluation
We study the problem of off-policy policy evaluation (OPPE) in RL.
Weighted Tensor Decomposition for Learning Latent Variables with Partial Data
Tensor decomposition methods are popular tools for learning latent variables given only lower-order moments of the data.
