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 …
Learning robotic navigation from experience: principles, methods and recent results
Navigation is one of the most heavily studied problems in robotics and is conventionally
approached as a geometric mapping and planning problem. However, real-world navigation …
approached as a geometric mapping and planning problem. However, real-world navigation …
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 …
Affordances from human videos as a versatile representation for robotics
Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …
several vision problems. However, despite some successful results on static datasets, it …
Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions
In this work, we present a scalable reinforcement learning method for training multi-task
policies from large offline datasets that can leverage both human demonstrations and …
policies from large offline datasets that can leverage both human demonstrations and …
Vip: Towards universal visual reward and representation via value-implicit pre-training
Reward and representation learning are two long-standing challenges for learning an
expanding set of robot manipulation skills from sensory observations. Given the inherent …
expanding set of robot manipulation skills from sensory observations. Given the inherent …
Rambo-rl: Robust adversarial model-based offline reinforcement learning
Offline reinforcement learning (RL) aims to find performant policies from logged data without
further environment interaction. Model-based algorithms, which learn a model of the …
further environment interaction. Model-based algorithms, which learn a model of the …
CORL: Research-oriented deep offline reinforcement learning library
CORL is an open-source library that provides thoroughly benchmarked single-file
implementations of both deep offline and offline-to-online reinforcement learning algorithms …
implementations of both deep offline and offline-to-online reinforcement learning algorithms …
Q-learning decision transformer: Leveraging dynamic programming for conditional sequence modelling in offline rl
T Yamagata, A Khalil… - … on Machine Learning, 2023 - proceedings.mlr.press
Recent works have shown that tackling offline reinforcement learning (RL) with a conditional
policy produces promising results. The Decision Transformer (DT) combines the conditional …
policy produces promising results. The Decision Transformer (DT) combines the conditional …
Offline rl policies should be trained to be adaptive
Offline RL algorithms must account for the fact that the dataset they are provided may leave
many facets of the environment unknown. The most common way to approach this challenge …
many facets of the environment unknown. The most common way to approach this challenge …