Learning Graphical Models
Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
While humans accomplish 72.4% of the tasks, the best
Predicting Future Actions of Reinforcement Learning Agents
Furthermore, using internal plans proves more robust to model quality compared to simulation-based approaches when predicting actions, while the results for event prediction are more mixed.
Probabilistic size-and-shape functional mixed models
It appears that individuals experience different numbers of small and large growth spurts that differ in magnitude and timing.
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate Fan-Ming Luo 1,2 Zuolin Tu
Recent progress has demonstrated that recurrent reinforcement learning (RL), which consists of a context encoder based on recurrent neural networks (RNNs) for unobservable state prediction and a multilayer perceptron (MLP) policy for decision making, can mitigate partial observability and serve as a robust baseline for POMDP tasks.
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model
Figure 1: Complex decision problems that require a good understanding of the environment's
Overleaf Example
Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance.
