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Found 11 papers, 4 papers with code
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning
Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards.
Visual Hindsight Self-Imitation Learning for Interactive Navigation
Interactive visual navigation tasks, which involve following instructions to reach and interact with specific targets, are challenging not only because successful experiences are very rare but also because the complex visual inputs require a substantial number of samples.
Neural Collage Transfer: Artistic Reconstruction via Material Manipulation
Collage is a creative art form that uses diverse material scraps as a base unit to compose a single image.
L-SA: Learning Under-Explored Targets in Multi-Target Reinforcement Learning
Tasks that involve interaction with various targets are called multi-target tasks.
On the Importance of Critical Period in Multi-stage Reinforcement Learning
The initial years of an infant's life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plasticity.
From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching Agent
We present an automated learning framework for a robotic sketching agent that is capable of learning stroke-based rendering and motor control simultaneously.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents
To validate this hypothesis, we adapt this notion of critical periods to learning in AI agents and investigate the critical period in the virtual environment for AI agents.
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning
Learning in a multi-target environment without prior knowledge about the targets requires a large amount of samples and makes generalization difficult.
Toward a Human-Level Video Understanding Intelligence
In this paper, we propose the Video Turing Test to provide effective and practical assessments of video understanding intelligence as well as human-likeness evaluation of AI agents.
CogME: A Cognition-Inspired Multi-Dimensional Evaluation Metric for Story Understanding
We introduce CogME, a cognition-inspired, multi-dimensional evaluation metric designed for AI models focusing on story understanding.
DramaQA: Character-Centered Video Story Understanding with Hierarchical QA
Despite recent progress on computer vision and natural language processing, developing a machine that can understand video story is still hard to achieve due to the intrinsic difficulty of video story.
