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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 701–725 of 2122 papers

TitleStatusHype
An Imitation Learning Approach for Cache Replacement—0
Dissipative Imitation Learning for Robust Dynamic Output Feedback—0
Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets—0
Discriminator-Guided Model-Based Offline Imitation Learning—0
Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation—0
BOSS: Benchmark for Observation Space Shift in Long-Horizon Task—0
A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning—0
Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving—0
Discovering hierarchies using Imitation Learning from hierarchy aware policies—0
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning—0
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight—0
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training—0
A New Framework for Query Efficient Active Imitation Learning—0
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information—0
DINO Pre-training for Vision-based End-to-end Autonomous Driving—0
DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space—0
A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing—0
A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills—0
3D Ego-Pose Estimation via Imitation Learning—0
DIGIC: Domain Generalizable Imitation Learning by Causal Discovery—0
Diffusion-Reward Adversarial Imitation Learning—0
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning—0
Diffusion Models for Robotic Manipulation: A Survey—0
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