Pieter Abbeel
(9 articles)
RL
Reinforcement Learning
Reinforcement Learning
Type of ML where an agent learns to make decisions by performing actions in an environment to achieve a goal, guided by rewards.
Generality: 890
Motor Learning
Process by which robots or AI systems acquire, refine, and optimize motor skills through experience and practice.
Generality: 675
Policy Gradient
Class of algorithms in RL that optimizes the parameters of a policy directly through gradient ascent on expected future rewards.
Generality: 675
IRL
Inverse Reinforcement Learning
Inverse Reinforcement Learning
Technique in which an algorithm learns the underlying reward function of an environment based on observed behavior from an agent, essentially inferring the goals an agent is trying to achieve.
Generality: 658
DRL
Deep Reinforcement Learning
Deep Reinforcement Learning
Combines neural networks with a reinforcement learning framework, enabling AI systems to learn optimal actions through trial and error to maximize a cumulative reward.
Generality: 855
RLHF
Reinforcement Learning from Human Feedback
Reinforcement Learning from Human Feedback
Technique that combines reinforcement learning (RL) with human feedback to guide the learning process towards desired outcomes.
Generality: 625
Imitation Learning
AI technique where models learn to perform tasks by mimicking human behavior or strategies demonstrated in training data.
Generality: 850
Sample Efficiency
Ability of a ML model to achieve high performance with a relatively small number of training samples.
Generality: 815
Diffusion
Class of generative models used to create high-quality, diverse samples of data by iteratively adding and then reversing noise.
Generality: 715