Search Results for author:
Found 12 papers, 4 papers with code
The Essential Role of Causality in Foundation World Models for Embodied AI
This paper focuses on the prospects of building foundation world models for the upcoming generation of embodied agents and presents a novel viewpoint on the significance of causality within these.
Hierarchical Imitation Learning for Stochastic Environments
However, such methods are often inappropriate for stochastic environments where the agent must also react to external factors: because agent types are inferred from the observed future trajectory during training, these environments require that the contributions of internal and external factors to the agent behaviour are disentangled and only internal factors, i. e., those under the agent's control, are encoded in the type.
Improving Spoken Language Identification with Map-Mix
The pre-trained multi-lingual XLSR model generalizes well for language identification after fine-tuning on unseen languages.
Foundation Models for Semantic Novelty in Reinforcement Learning
Effectively exploring the environment is a key challenge in reinforcement learning (RL).
Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model
In this work, we propose a bi-encoder transformer mixture model for speaker age and height estimation.
Generalization in Cooperative Multi-Agent Systems
Specifically, we study generalization bounds under a linear dependence of the underlying dynamics on the agent capabilities, which can be seen as a generalization of Successor Features to MAS.
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients
Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenarios.
Excitation energies through Becke's exciton model within a Cartesian-grid KS DFT
Photon-induced electronic excitations are ubiquitously observed in organic chromophore.
Chemical Physics
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function.
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
VDN and QMIX are two popular value-based algorithms for cooperative MARL that learn a centralized action value function as a monotonic mixing of per-agent utilities.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
RODE: Learning Roles to Decompose Multi-Agent Tasks
Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces.
Factor Analysis in Fault Diagnostics Using Random Forest
Factor analysis or sometimes referred to as variable analysis has been extensively used in classification problems for identifying specific factors that are significant to particular classes.
