Abstract: Recent research has shown that deep learning models are likely to make incorrect predictions even when exposed to minor perturbations. To address this, training models on adversarial ...
From Sparse to Dense: Toddler–inspired Reward Transition in Goal–Oriented Reinforcement Learning
Abstract: Reinforcement learning (RL) agents face fundamental challenges in balancing exploration and exploitation, particularly when sparse or dense rewards bias learning toward sub–optimal behaviors ...
coffee_shop_system/ ├── __init__.py ├── models/ # Data models │ ├── __init__.py │ ├── product.py # Base & derived product classes ...
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