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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 1051–1075 of 2122 papers

TitleStatusHype
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations—0
Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving—0
Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion—0
Probability Density Estimation Based Imitation Learning—0
Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learning—0
Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets—0
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation—0
Progressively Efficient Learning—0
ProgRM: Build Better GUI Agents with Progress Rewards—0
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation—0
Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs—0
Provable Representation Learning for Imitation with Contrastive Fourier Features—0
Provable Representation Learning for Imitation Learning via Bi-level Optimization—0
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally—0
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation—0
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees—0
Provably Efficient Third-Person Imitation from Offline Observation—0
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions—0
Quality Diversity Imitation Learning—0
Quantile Filtered Imitation Learning—0
Quantization-Aware Imitation-Learning for Resource-Efficient Robotic Control—0
Quantum Imitation Learning—0
RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning—0
RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learning—0
RadGrad: Active learning with loss gradients—0
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