Examples of Reinforcement Learning: High computational cost: RL often requires significant computational resources, especially when dealing with complex environments or tasks. Training agents can ...

In my opinion, the future belongs instead to hyperspecialized AI models that are tailored to excel in hyper-specific domains.

In this example, the reward is staying upright, while the punishment is falling. Based on the feedback the robot receives for its actions, optimal actions get reinforced. Reinforcement learning ...

Most machine learning algorithms are shouting names in the street. They perform perceptive tasks that a person can do in under a second. But another kind of AI — deep reinforcement learning ...

Reinforcement Learning does NOT make the base model more intelligent and limits the world of the base model in exchange for early pass performances. Graphs show that after pass 1000 the reasoning ...

Research team introduced clustered reinforcement learning (CRL), a novel RL framework for efficient exploration in large state spaces or sparse ...

Two recent papers in Science Robotics highlight how that type of AI — called reinforcement learning — could make such robots a reality. “We’ve seen really wonderful progress in AI in the ...

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...