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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 1751–1775 of 2122 papers

TitleStatusHype
Differentiable Robust LQR Layers—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
Diffusion-Based Imitation Learning for Social Pose Generation—0
Diffusion Imitation from Observation—0
Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning—0
Diffusion Model-Augmented Behavioral Cloning—0
Diffusion Models for Robotic Manipulation: A Survey—0
Diffusion-Reward Adversarial Imitation Learning—0
DIGIC: Domain Generalizable Imitation Learning by Causal Discovery—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models—0
DINO Pre-training for Vision-based End-to-end Autonomous Driving—0
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information—0
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training—0
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning—0
Discovering hierarchies using Imitation Learning from hierarchy aware policies—0
Discriminator-Guided Model-Based Offline Imitation Learning—0
Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets—0
Dissipative Imitation Learning for Robust Dynamic Output Feedback—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods—0
Distilling Realizable Students from Unrealizable Teachers—0
Distributional Decision Transformer for Hindsight Information Matching—0
Distributionally Robust Imitation Learning—0
Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks—0
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