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Found 18 papers, 5 papers with code
Mind-to-Image: Projecting Visual Mental Imagination of the Brain from fMRI
The reconstruction of images observed by subjects from fMRI data collected during visual stimuli has made significant strides in the past decade, thanks to the availability of extensive fMRI datasets and advancements in generative models for image generation.
Utility-based Adaptive Teaching Strategies using Bayesian Theory of Mind
To this end, human teachers seem to build mental models of the learner's internal state, a capacity known as Theory of Mind (ToM).
Enhancing Agent Communication and Learning through Action and Language
We introduce a novel category of GC-agents capable of functioning as both teachers and learners.
Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning
Teaching an agent to perform new tasks using natural language can easily be hindered by ambiguities in interpretation.
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments
In this paper, we implement pedagogy and pragmatism mechanisms by leveraging a Bayesian model of Goal Inference from demonstrations (BGI).
Pedagogical Demonstrations and Pragmatic Learning in Artificial Tutor-Learner Interactions
When demonstrating a task, human tutors pedagogically modify their behavior by either "showing" the task rather than just "doing" it (exaggerating on relevant parts of the demonstration) or by giving demonstrations that best disambiguate the communicated goal.
Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?
We conclude that the problem of supervised affordance segmentation is included in the problem of object segmentation and argue that better benchmarks for affordance learning should include action capacities.
SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences
We introduce SCOD (Sensory Commutativity Object Detection), an active method for movable and immovable object detection.
Towards Teachable Autotelic Agents
In the field of Artificial Intelligence, these extremes respectively map to autonomous agents learning from their own signals and interactive learning agents fully taught by their teachers.
On the Sensory Commutativity of Action Sequences for Embodied Agents
In such case, for autonomous embodied agents with first-person sensors, perception can be learned end-to-end to solve particular tasks.
DisCoRL: Continual Reinforcement Learning via Policy Distillation
In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of those policies applying without an external signal.
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
We focus on the problem of teaching a robot to solve tasks presented sequentially, i. e., in a continual learning scenario.
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Finding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construction of data-efficient autonomous agents.
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge, and then use Reinforcement Learning on the resulting features for efficient policy learning.
Generative Models from the perspective of Continual Learning
We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10).
Continual State Representation Learning for Reinforcement Learning using Generative Replay
As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge.
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents.
Word2Vec applied to Recommendation: Hyperparameters Matter
Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create item embeddings with successful applications in recommendation.
