Search Results for author:
Found 11 papers, 3 papers with code
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation
Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement.
Benchmarking Constraint Inference in Inverse Reinforcement Learning
When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints.
Learning Soft Constraints From Constrained Expert Demonstrations
We consider the setting where the reward function is given, and the constraints are unknown, and propose a method that is able to recover these constraints satisfactorily from the expert data.
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning
We propose a Represent And Mimic (RAMi) framework for training 1) an identifiable latent representation to capture the independent factors of variation for the objects and 2) a mimic tree that extracts the causal impact of the latent features on DRL action values.
Distributional Reinforcement Learning with Monotonic Splines
Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the distribution over returns to capture the intrinsic uncertainty of MDPs.
Distributional Reinforcement Learning
reinforcement-learning
+1
Learning Object-Oriented Dynamics for Planning from Text
The advancement of dynamics models enables model-based planning in complex environments.
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge
We describe NTS-NOTEARS, a score-based structure learning method for time-series data to learn dynamic Bayesian networks (DBNs) that captures nonlinear, lagged (inter-slice) and instantaneous (intra-slice) relations among variables.
Learning Agent Representations for Ice Hockey
This paper develops a new approach for agent representations, based on a Markov game model, that is tailored towards applications in professional ice hockey.
Cracking the Black Box: Distilling Deep Sports Analytics
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics.
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees
An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment.
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation
To assess a player's overall performance, we introduce a novel Game Impact Metric (GIM) that aggregates the values of the player's actions.
