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Imitation Learning

Imitation Learning is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal.

Finally, a newer methodology, Inverse Q-Learning aims at directly learning Q-functions from expert data, implicitly representing rewards, under which the optimal policy can be given as a Boltzmann distribution similar to soft Q-learning

Source: Learning to Imitate

Papers

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Showing 721–730 of 2122 papers

TitleStatusHype
Generative Intrinsic Optimization: Intrinsic Control with Model Learning—0
Cross-Episodic Curriculum for Transformer Agents—0
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning—0
RoboCLIP: One Demonstration is Enough to Learn Robot PoliciesCode1
Imitation Learning from Purified DemonstrationsCode0
Imitation Learning from Observation with Automatic Discount SchedulingCode1
Zero-Shot Transfer in Imitation Learning—0
Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning—0
Memory-Consistent Neural Networks for Imitation Learning—0
Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and BeyondCode1
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