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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 1151–1175 of 2122 papers

TitleStatusHype
Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback—0
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models—0
Follow the Clairvoyant: an Imitation Learning Approach to Optimal ControlCode0
Out-of-Dynamics Imitation Learning from Multimodal DemonstrationsCode0
NeuroCERIL: Robotic Imitation Learning via Hierarchical Cause-Effect Reasoning in Programmable Attractor Neural NetworksCode0
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning—0
ProtoX: Explaining a Reinforcement Learning Agent via PrototypingCode0
Deconfounding Imitation Learning with Variational InferenceCode0
Beyond spiking networks: the computational advantages of dendritic amplification and input segregationCode0
Learning Modular Robot Locomotion from Demonstrations—0
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning—0
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games—0
Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative ReasoningCode0
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning—0
Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning—0
Text Editing as Imitation GameCode0
Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR StreamingCode0
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control—0
Robust Imitation via Mirror Descent Inverse Reinforcement Learning—0
Learning and Retrieval from Prior Data for Skill-based Imitation Learning—0
NIFT: Neural Interaction Field and Template for Object Manipulation—0
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving—0
Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning—0
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback—0
Robust Imitation of a Few Demonstrations with a Backwards Model—0
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