Towards continual reinforcement learning: A review and perspectives
In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
Anytime-valid off-policy inference for contextual bandits
Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in
healthcare and the tech industry. They involve online learning algorithms that adaptively …
healthcare and the tech industry. They involve online learning algorithms that adaptively …
[图书][B] Distributional reinforcement learning
The first comprehensive guide to distributional reinforcement learning, providing a new
mathematical formalism for thinking about decisions from a probabilistic perspective …
mathematical formalism for thinking about decisions from a probabilistic perspective …
Proximal reinforcement learning: Efficient off-policy evaluation in partially observed markov decision processes
In applications of offline reinforcement learning to observational data, such as in healthcare
or education, a general concern is that observed actions might be affected by unobserved …
or education, a general concern is that observed actions might be affected by unobserved …
Distributional offline policy evaluation with predictive error guarantees
We study the problem of estimating the distribution of the return of a policy using an offline
dataset that is not generated from the policy, ie, distributional offline policy evaluation (OPE) …
dataset that is not generated from the policy, ie, distributional offline policy evaluation (OPE) …
Conformal off-policy prediction in contextual bandits
Most off-policy evaluation methods for contextual bandits have focused on the expected
outcome of a policy, which is estimated via methods that at best provide only asymptotic …
outcome of a policy, which is estimated via methods that at best provide only asymptotic …
Allsim: Simulating and benchmarking resource allocation policies in multi-user systems
Numerous real-world systems, ranging from healthcare to energy grids, involve users
competing for finite and potentially scarce resources. Designing policies for resource …
competing for finite and potentially scarce resources. Designing policies for resource …
Hope: Human-centric off-policy evaluation for e-learning and healthcare
Reinforcement learning (RL) has been extensively researched for enhancing human-
environment interactions in various human-centric tasks, including e-learning and …
environment interactions in various human-centric tasks, including e-learning and …
Opportunities and challenges from using animal videos in reinforcement learning for navigation
We investigate the use of animal videos (observations) to improve Reinforcement Learning
(RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by …
(RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by …
Off-policy evaluation for action-dependent non-stationary environments
Methods for sequential decision-making are often built upon a foundational assumption that
the underlying decision process is stationary. This limits the application of such methods …
the underlying decision process is stationary. This limits the application of such methods …