Anti-exploration by random network distillation
Despite the success of Random Network Distillation (RND) in various domains, it was shown
as not discriminative enough to be used as an uncertainty estimator for penalizing out-of …
as not discriminative enough to be used as an uncertainty estimator for penalizing out-of …
Revisiting the minimalist approach to offline reinforcement learning
Recent years have witnessed significant advancements in offline reinforcement learning
(RL), resulting in the development of numerous algorithms with varying degrees of …
(RL), resulting in the development of numerous algorithms with varying degrees of …
Understanding, predicting and better resolving Q-value divergence in offline-RL
The divergence of the Q-value estimation has been a prominent issue offline reinforcement
learning (offline RL), where the agent has no access to real dynamics. Traditional beliefs …
learning (offline RL), where the agent has no access to real dynamics. Traditional beliefs …
XLand-minigrid: Scalable meta-reinforcement learning environments in JAX
We present XLand-MiniGrid, a suite of tools and grid-world environments for meta-
reinforcement learning research inspired by the diversity and depth of XLand and the …
reinforcement learning research inspired by the diversity and depth of XLand and the …
: On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics
Autonomous robotic systems, like autonomous vehicles and robotic search and rescue,
require efficient on-device training for continuous adaptation of Deep Reinforcement …
require efficient on-device training for continuous adaptation of Deep Reinforcement …
NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation
Mobile devices such as smartphones, laptops, and tablets can often connect to multiple
access networks (eg, Wi-Fi, LTE, and 5G) simultaneously. Recent advancements facilitate …
access networks (eg, Wi-Fi, LTE, and 5G) simultaneously. Recent advancements facilitate …
Data-driven hierarchical learning approach for multi-point servo control of Pan–Tilt–Zoom cameras
Abstract Pan–Tilt–Zoom (PTZ) cameras, with their significant features of free rotation and
zoom, are widely used in areas such as border security, ecological conservation, emergency …
zoom, are widely used in areas such as border security, ecological conservation, emergency …
The role of deep learning regularizations on actors in offline rl
Deep learning regularization techniques, such as dropout, layer normalization, or weight
decay, are widely adopted in the construction of modern artificial neural networks, often …
decay, are widely adopted in the construction of modern artificial neural networks, often …
Judgmentally adjusted Q-values based on Q-ensemble for offline reinforcement learning
W Liu, S Xiang, T Zhang, Y Han, X Guo… - Neural Computing and …, 2024 - Springer
Recent advancements in offline reinforcement learning (offline RL) have leveraged the Q-
ensemble approach to derive optimal policies from static datasets collected in the past. By …
ensemble approach to derive optimal policies from static datasets collected in the past. By …
Dataset Clustering for Improved Offline Policy Learning
Q Wang, Y Deng, FR Sanchez, K Wang… - arXiv preprint arXiv …, 2024 - arxiv.org
Offline policy learning aims to discover decision-making policies from previously-collected
datasets without additional online interactions with the environment. As the training dataset …
datasets without additional online interactions with the environment. As the training dataset …