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 …
Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
Planning with diffusion for flexible behavior synthesis
Model-based reinforcement learning methods often use learning only for the purpose of
estimating an approximate dynamics model, offloading the rest of the decision-making work …
estimating an approximate dynamics model, offloading the rest of the decision-making work …
Is conditional generative modeling all you need for decision-making?
Recent improvements in conditional generative modeling have made it possible to generate
high-quality images from language descriptions alone. We investigate whether these …
high-quality images from language descriptions alone. We investigate whether these …
Diffusion policies as an expressive policy class for offline reinforcement learning
Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously
collected static dataset, is an important paradigm of RL. Standard RL methods often perform …
collected static dataset, is an important paradigm of RL. Standard RL methods often perform …
Decision transformer: Reinforcement learning via sequence modeling
We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence
modeling problem. This allows us to draw upon the simplicity and scalability of the …
modeling problem. This allows us to draw upon the simplicity and scalability of the …
Offline reinforcement learning as one big sequence modeling problem
Reinforcement learning (RL) is typically viewed as the problem of estimating single-step
policies (for model-free RL) or single-step models (for model-based RL), leveraging the …
policies (for model-free RL) or single-step models (for model-based RL), leveraging the …
Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning
A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization
from existing datasets followed by fast online fine-tuning with limited interaction. However …
from existing datasets followed by fast online fine-tuning with limited interaction. However …
Uncertainty-based offline reinforcement learning with diversified q-ensemble
Offline reinforcement learning (offline RL), which aims to find an optimal policy from a
previously collected static dataset, bears algorithmic difficulties due to function …
previously collected static dataset, bears algorithmic difficulties due to function …
Is pessimism provably efficient for offline rl?
We study offline reinforcement learning (RL), which aims to learn an optimal policy based on
a dataset collected a priori. Due to the lack of further interactions with the environment …
a dataset collected a priori. Due to the lack of further interactions with the environment …