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Found 6 papers, 3 papers with code
Generalized Neural Policies for Relational MDPs
We present the first neural approach for solving RMDPs, expressed in the probabilistic planning language of RDDL.
Finetune like you pretrain: Improved finetuning of zero-shot vision models
In total, these benchmarks establish contrastive finetuning as a simple, intuitive, and state-of-the-art approach for supervised finetuning of image-text models like CLIP.
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
We show that if the information contained in the graph and the time series data are closely related, then this inter-dependence can be used to predict the time series with improved accuracy.
Symbolic Network: Generalized Neural Policies for Relational MDPs
We present SymNet, the first neural approach for solving RMDPs that are expressed in the probabilistic planning language of RDDL.
Size Independent Neural Transfer for RDDL Planning
To mitigate this, recent work has studied neural transfer learning, so that a generic planner trained on other problems of the same domain can rapidly transfer to a new problem.
Transfer of Deep Reactive Policies for MDP Planning
We then learn an RL agent in the embedding space, making a near zero-shot transfer possible, i. e., without much training on the new instance, and without using the domain simulator at all.
