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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 1226–1250 of 2122 papers

TitleStatusHype
MDPFuzz: Testing Models Solving Markov Decision Processes—0
Guided Imitation of Task and Motion Planning—0
Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning—0
CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation TasksCode1
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation—0
The Surprising Effectiveness of Representation Learning for Visual ImitationCode1
Quantile Filtered Imitation Learning—0
Document Level Hierarchical Transformer—0
A General Language Assistant as a Laboratory for AlignmentCode2
Solving Graph-based Public Goods Games with Tree Search and Imitation LearningCode0
Distributionally Robust Imitation Learning—0
On the Value of Interaction and Function Approximation in Imitation Learning—0
Generalizable Imitation Learning from Observation via Inferring Goal Proximity—0
Curriculum Offline Imitating Learning—0
Dynamic Inference—0
Back to Reality for Imitation Learning—0
Neural Column Generation for Capacitated Vehicle Routing—0
Sample Efficient Imitation Learning via Reward Function Trained in Advance—0
Generalized Decision Transformer for Offline Hindsight Information MatchingCode1
NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems—0
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition—0
Improving Learning from Demonstrations by Learning from Experience—0
Learning Multi-Stage Tasks with One Demonstration via Self-Replay—0
Model-Based Reinforcement Learning via Stochastic Hybrid Models—0
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot ManipulationCode1
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