SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1351–1375 of 2122 papers

TitleStatusHype
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning—0
Guided Imitation of Task and Motion Planning—0
Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning—0
MDPFuzz: Testing Models Solving Markov Decision Processes—0
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation—0
Quantile Filtered Imitation Learning—0
Distributionally Robust Imitation Learning—0
Generalizable Imitation Learning from Observation via Inferring Goal Proximity—0
Curriculum Offline Imitating Learning—0
Document Level Hierarchical Transformer—0
On the Value of Interaction and Function Approximation in Imitation Learning—0
Solving Graph-based Public Goods Games with Tree Search and Imitation LearningCode0
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
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition—0
NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems—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
Off-policy Imitation Learning from Visual Inputs—0
Smooth Imitation Learning via Smooth Costs and Smooth Policies—0
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies—0
Show:102550
← PrevPage 55 of 85Next →

No leaderboard results yet.