Transformers in reinforcement learning: a survey
P Agarwal, AA Rahman, PL St-Charles… - arXiv preprint arXiv …, 2023 - arxiv.org
Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …
computer vision, and robotics, where they improve performance compared to other neural …
Distributionally robust offline reinforcement learning with linear function approximation
Among the reasons hindering reinforcement learning (RL) applications to real-world
problems, two factors are critical: limited data and the mismatch between the testing …
problems, two factors are critical: limited data and the mismatch between the testing …
Provably efficient offline reinforcement learning for partially observable markov decision processes
We study offline reinforcement learning (RL) for partially observable Markov decision
processes (POMDPs) with possibly infinite state and observation spaces. Under the …
processes (POMDPs) with possibly infinite state and observation spaces. Under the …
An empirical study of implicit regularization in deep offline rl
C Gulcehre, S Srinivasan, J Sygnowski… - arXiv preprint arXiv …, 2022 - arxiv.org
Deep neural networks are the most commonly used function approximators in offline
reinforcement learning. Prior works have shown that neural nets trained with TD-learning …
reinforcement learning. Prior works have shown that neural nets trained with TD-learning …
Boosting offline reinforcement learning for autonomous driving with hierarchical latent skills
Learning-based vehicle planning is receiving increasing attention with the emergence of
diverse driving simulators and large-scale driving datasets. While offline reinforcement …
diverse driving simulators and large-scale driving datasets. While offline reinforcement …
A model-based solution to the offline multi-agent reinforcement learning coordination problem
Training multiple agents to coordinate is an essential problem with applications in robotics,
game theory, economics, and social sciences. However, most existing Multi-Agent …
game theory, economics, and social sciences. However, most existing Multi-Agent …
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments
Offline Reinforcement Learning (RL) has emerged as a promising framework for learning
policies without active interactions, making it especially appealing for autonomous driving …
policies without active interactions, making it especially appealing for autonomous driving …
Optimizing traffic control with model-based learning: A pessimistic approach to data-efficient policy inference
Traffic signal control is an important problem in urban mobility with a significant potential for
economic and environmental impact. While there is a growing interest in Reinforcement …
economic and environmental impact. While there is a growing interest in Reinforcement …
Lyapunov stability regulation of deep reinforcement learning control with application to automated driving
Reinforcement learning (RL) control for nonlinear dynamical systems has seen increasing
interests in recent years. However, these methods have limited practical use due to the lack …
interests in recent years. However, these methods have limited practical use due to the lack …
NondBREM: Nondeterministic Offline Reinforcement Learning for Large-Scale Order Dispatching
One of the most important tasks in ride-hailing is order dispatching, ie, assigning unserved
orders to available drivers. Recent order dispatching has achieved a significant …
orders to available drivers. Recent order dispatching has achieved a significant …