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Found 10 papers, 5 papers with code
SARC: Soft Actor Retrospective Critic
The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual consistency between the two.
HyHTM: Hyperbolic Geometry based Hierarchical Topic Models
We present HyHTM - a Hyperbolic geometry based Hierarchical Topic Models - that addresses these limitations by incorporating hierarchical information from hyperbolic geometry to explicitly model hierarchies in topic models.
Video2Skill: Adapting Events in Demonstration Videos to Skills in an Environment using Cyclic MDP Homomorphisms
Humans excel at learning long-horizon tasks from demonstrations augmented with textual commentary, as evidenced by the burgeoning popularity of tutorial videos online.
Information-theoretic Evolution of Model Agnostic Global Explanations
Our approach builds on top of existing local model explanation methods to extract conditions important for explaining model behavior for specific instances followed by an evolutionary algorithm that optimizes an information theory based fitness function to construct rules that explain global model behavior.
SHERLock: Self-Supervised Hierarchical Event Representation Learning
Also, they are believed to be arranged hierarchically, allowing for an efficient representation of complex long-horizon experiences.
MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance
Training a classification model on a dataset where the instances of one class outnumber those of the other class is a challenging problem.
Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency
We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches.
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss
We show how agents trained with SQLoss evolve cooperative behavior in several social dilemma matrix games.
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches.
MAGIX: Model Agnostic Globally Interpretable Explanations
Explaining the behavior of a black box machine learning model at the instance level is useful for building trust.
