Search Results for author: Wilka Carvalho

Found 5 papers, 4 papers with code

Composing Task Knowledge with Modular Successor Feature Approximators

1 code implementation28 Jan 2023 Wilka Carvalho, Angelos Filos, Richard L. Lewis, Honglak Lee, Satinder Singh

Recently, the Successor Features and Generalized Policy Improvement (SF&GPI) framework has been proposed as a method for learning, composing, and transferring predictive knowledge and behavior.

Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning

1 code implementation NeurIPS 2021 Christopher Hoang, Sungryull Sohn, Jongwook Choi, Wilka Carvalho, Honglak Lee

SFL leverages the ability of successor features (SF) to capture transition dynamics, using it to drive exploration by estimating state-novelty and to enable high-level planning by abstracting the state-space as a non-parametric landmark-based graph.

Efficient Exploration reinforcement-learning +1

Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment

no code implementations28 Oct 2020 Wilka Carvalho, Anthony Liang, Kimin Lee, Sungryull Sohn, Honglak Lee, Richard L. Lewis, Satinder Singh

In this work, we show that one can learn object-interaction tasks from scratch without supervision by learning an attentive object-model as an auxiliary task during task learning with an object-centric relational RL agent.

Reinforcement Learning (RL) Representation Learning

Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering

1 code implementation9 Nov 2018 Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, Biplav Srivastava

While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern.


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