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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 1651–1675 of 2122 papers

TitleStatusHype
Coarse-to-Fine 3D Keyframe Transporter—0
Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration—0
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories—0
CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity—0
Co-Imitation: Learning Design and Behaviour by Imitation—0
Co-Imitation Learning without Expert Demonstration—0
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy—0
Collaborating Action by Action: A Multi-agent LLM Framework for Embodied Reasoning—0
Combating False Negatives in Adversarial Imitation Learning—0
Combining Imitation and Reinforcement Learning with Free Energy Principle—0
Complex Skill Acquisition Through Simple Skill Imitation Learning—0
Complex Skill Acquisition through Simple Skill Imitation Learning—0
Compressed imitation learning—0
Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification—0
ConBaT: Control Barrier Transformer for Safe Policy Learning—0
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint—0
Concurrent Training Improves the Performance of Behavioral Cloning from Observation—0
Conditional Driving from Natural Language Instructions—0
Conditional Imitation Learning for Multi-Agent Games—0
Conditional Kernel Imitation Learning for Continuous State Environments—0
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
Connecting the Dots Between MLE and RL for Sequence Prediction—0
Context-Former: Stitching via Latent Conditioned Sequence Modeling—0
Contextual Bandits and Imitation Learning via Preference-Based Active Queries—0
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