Results 281 to 290 of about 1,301,436 (336)

TNCOA: Efficient Exploration via Observation‐Action Constraint on Trajectory‐Based Intrinsic Reward

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Efficient exploration is critical in handling sparse rewards and partial observability in deep reinforcement learning. However, most existing intrinsic reward methods based on novelty rely on single‐step observations or Euclidean distances.
Jingxiang Ma, Hongbin Ma, Youzhi Zhang
wiley   +1 more source

Temporal Dependency‐Aware Trajectory‐Level Behavioural Metric for Exploration in Reinforcement Learning

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Intrinsic motivation serves as the predominant paradigm of exploration in reinforcement learning. In pursuit of an informative and robust state representation, the behavioural metric groups behaviourally equivalent states together, which share the same single‐step reward and transition distribution.
Anjie Zhu   +3 more
wiley   +1 more source

Intrusion of quantum crystallography into classical lands. [PDF]

open access: yesActa Crystallogr B Struct Sci Cryst Eng Mater
Yu S, Gillet JM.
europepmc   +1 more source

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