Look beneath the surface: Exploiting fundamental symmetry for sample-efficient offline rl
Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by
learning policies from pre-collected datasets without interacting with the environment …
learning policies from pre-collected datasets without interacting with the environment …
Exploration and anti-exploration with distributional random network distillation
Exploration remains a critical issue in deep reinforcement learning for an agent to attain high
returns in unknown environments. Although the prevailing exploration Random Network …
returns in unknown environments. Although the prevailing exploration Random Network …
[HTML][HTML] Possibilities of reinforcement learning for nuclear power plants: Evidence on current applications and beyond
A Gong, Y Chen, J Zhang, X Li - Nuclear Engineering and Technology, 2024 - Elsevier
Nuclear energy plays a crucial role in energy supply in the 21st century, and more and more
Nuclear Power Plants (NPPs) will be in operation to contribute to the development of human …
Nuclear Power Plants (NPPs) will be in operation to contribute to the development of human …
Balancing therapeutic effect and safety in ventilator parameter recommendation: An offline reinforcement learning approach
Reinforcement learning (RL) is increasingly applied in recommending ventilator parameters,
yet existing methods prioritize therapeutic effect over patient safety. This leads to excessive …
yet existing methods prioritize therapeutic effect over patient safety. This leads to excessive …
A reliable representation with bidirectional transition model for visual reinforcement learning generalization
Visual reinforcement learning has proven effective in solving control tasks with high-
dimensional observations. However, extracting reliable and generalizable representations …
dimensional observations. However, extracting reliable and generalizable representations …
Uncertainty-driven trajectory truncation for data augmentation in offline reinforcement learning
Equipped with the trained environmental dynamics, model-based offline reinforcement
learning (RL) algorithms can often successfully learn good policies from fixed-sized …
learning (RL) algorithms can often successfully learn good policies from fixed-sized …
CROP: Conservative Reward for Model-based Offline Policy Optimization
Offline reinforcement learning (RL) aims to optimize policy using collected data without
online interactions. Model-based approaches are particularly appealing for addressing …
online interactions. Model-based approaches are particularly appealing for addressing …
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization
Y Zhai, Y Li, Z Gao, X Gong, K Xu, D Feng… - Proceedings of the …, 2024 - ojs.aaai.org
Model-based offline reinforcement learning (RL) has made remarkable progress, offering a
promising avenue for improving generalization with synthetic model rollouts. Existing works …
promising avenue for improving generalization with synthetic model rollouts. Existing works …
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Offline reinforcement learning (RL) has attracted much attention due to its ability in learning
from static offline datasets and eliminating the need of interacting with the environment …
from static offline datasets and eliminating the need of interacting with the environment …
Implicit policy constraint for offline reinforcement learning
Z Peng, Y Liu, C Han, Z Zhou - CAAI Transactions on …, 2024 - Wiley Online Library
Offline reinforcement learning (RL) aims to learn policies entirely from passively collected
datasets, making it a data‐driven decision method. One of the main challenges in offline RL …
datasets, making it a data‐driven decision method. One of the main challenges in offline RL …