059 — Ai Takeuchi Mird

| Term | Definition | |------|-------------| | | The simulation of human intelligence processes by machines, especially computer systems. | | Machine Learning (ML) | A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. | | Reinforcement Learning (RL) | An area of ML where an agent learns to make decisions by taking actions in an environment to maximize a cumulative reward. | | Inverse Reinforcement Learning (IRL) | A form of RL where the goal is to infer the reward function from observing an expert's behavior. | | Multi-modal Learning | A subfield of ML that aims to build models that can process and relate information from multiple modalities (e.g., text, image, audio). | | Mutual Information | A measure of the mutual dependence between two variables. In the MIRD framework, minimizing this helps remove redundant information. | | Reward Hacking | A phenomenon in RL where an agent finds a way to achieve a high reward by exploiting a poorly specified reward function, without actually performing the desired task. | | Federated Learning | A ML technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. | | IEEE 802.11ah | A wireless networking standard (also known as Wi-Fi HaLow) that operates in the sub-1 GHz band, designed for low-power, long-range IoT applications. |

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This public link is valid for 7 days and shares a thread, including any personal information you added. This link or copies made by others cannot be deleted. If you share with third parties, their policies apply. Can’t copy the link right now. Try again later. | Term | Definition | |------|-------------| | |

MIRD in reinforcement learning is a framework designed to infer transferable reward functions. It addresses a core challenge in , where the goal is to learn the underlying reward function from demonstrations of expert behavior. | | Inverse Reinforcement Learning (IRL) | A

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