Improving and benchmarking offline reinforcement learning algorithms
Recently, Offline Reinforcement Learning (RL) has achieved remarkable progress with the
emergence of various algorithms and datasets. However, these methods usually focus on …
emergence of various algorithms and datasets. However, these methods usually focus on …
Semi-supervised off-policy reinforcement learning and value estimation for dynamic treatment regimes
A Sonabend-W, N Laha, AN Ananthakrishnan… - Journal of Machine …, 2023 - jmlr.org
Reinforcement learning (RL) has shown great promise in estimating dynamic treatment
regimes which take into account patient heterogeneity. However, health-outcome …
regimes which take into account patient heterogeneity. However, health-outcome …
Minimax model learning
We present a novel off-policy loss function for learning a transition model in model-based
reinforcement learning. Notably, our loss is derived from the off-policy policy evaluation …
reinforcement learning. Notably, our loss is derived from the off-policy policy evaluation …
When is off-policy evaluation useful? a data-centric perspective
Evaluating the value of a hypothetical target policy with only a logged dataset is important
but challenging. On the one hand, it brings opportunities for safe policy improvement under …
but challenging. On the one hand, it brings opportunities for safe policy improvement under …
SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation
H Kiyohara, R Kishimoto, K Kawakami… - arXiv preprint arXiv …, 2023 - arxiv.org
This paper introduces SCOPE-RL, a comprehensive open-source Python software designed
for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection …
for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection …
Optimizing Representations and Policies for Question Sequencing Using Reinforcement Learning.
This paper studies the use of Reinforcement Learning (RL) policies for optimizing the
sequencing of online learning materials to students. Our approach provides an end to end …
sequencing of online learning materials to students. Our approach provides an end to end …
Off-policy evaluation with online adaptation for robot exploration in challenging environments
Autonomous exploration has many important applications. However, classic information
gain-based or frontier-based exploration only relies on the robot current state to determine …
gain-based or frontier-based exploration only relies on the robot current state to determine …
Accelerating offline reinforcement learning application in real-time bidding and recommendation: Potential use of simulation
In recommender systems (RecSys) and real-time bidding (RTB) for online advertisements,
we often try to optimize sequential decision making using bandit and reinforcement learning …
we often try to optimize sequential decision making using bandit and reinforcement learning …
Deep reinforcement learning approaches for technology enhanced learning
Z Li - 2023 - etheses.dur.ac.uk
Artificial Intelligence (AI) has advanced significantly in recent years, transforming various
industries and domains. Its ability to extract patterns and insights from large volumes of data …
industries and domains. Its ability to extract patterns and insights from large volumes of data …
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
In practice, it is essential to compare and rank candidate policies offline before real-world
deployment for safety and reliability. Prior work seeks to solve this offline policy ranking …
deployment for safety and reliability. Prior work seeks to solve this offline policy ranking …