A survey on offline reinforcement learning: Taxonomy, review, and open problems
RF Prudencio, MROA Maximo… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …
experienced a dramatic increase in popularity, scaling to previously intractable problems …
Offline rl without off-policy evaluation
D Brandfonbrener, W Whitney… - Advances in neural …, 2021 - proceedings.neurips.cc
Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-
critic approach involving off-policy evaluation. In this paper we show that simply doing one …
critic approach involving off-policy evaluation. In this paper we show that simply doing one …
Hyperparameter selection for offline reinforcement learning
Offline reinforcement learning (RL purely from logged data) is an important avenue for
deploying RL techniques in real-world scenarios. However, existing hyperparameter …
deploying RL techniques in real-world scenarios. However, existing hyperparameter …
Ten questions concerning reinforcement learning for building energy management
As buildings account for approximately 40% of global energy consumption and associated
greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The …
greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The …
Rl unplugged: A suite of benchmarks for offline reinforcement learning
Offline methods for reinforcement learning have a potential to help bridge the gap between
reinforcement learning research and real-world applications. They make it possible to learn …
reinforcement learning research and real-world applications. They make it possible to learn …
Model selection for offline reinforcement learning: Practical considerations for healthcare settings
Reinforcement learning (RL) can be used to learn treatment policies and aid decision
making in healthcare. However, given the need for generalization over complex state/action …
making in healthcare. However, given the need for generalization over complex state/action …
A workflow for offline model-free robotic reinforcement learning
Offline reinforcement learning (RL) enables learning control policies by utilizing only prior
experience, without any online interaction. This can allow robots to acquire generalizable …
experience, without any online interaction. This can allow robots to acquire generalizable …
Reinforcement learning in practice: Opportunities and challenges
Y Li - arXiv preprint arXiv:2202.11296, 2022 - arxiv.org
This article is a gentle discussion about the field of reinforcement learning in practice, about
opportunities and challenges, touching a broad range of topics, with perspectives and …
opportunities and challenges, touching a broad range of topics, with perspectives and …
Off-policy evaluation for large action spaces via conjunct effect modeling
We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action
spaces where conventional importance-weighting approaches suffer from excessive …
spaces where conventional importance-weighting approaches suffer from excessive …
Multi-task fusion via reinforcement learning for long-term user satisfaction in recommender systems
Recommender System (RS) is an important online application that affects billions of users
every day. The mainstream RS ranking framework is composed of two parts: a Multi-Task …
every day. The mainstream RS ranking framework is composed of two parts: a Multi-Task …