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 801–825 of 2122 papers

TitleStatusHype
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching—0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
One-shot Imitation Learning via Interaction Warping—0
Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication FrameworkCode1
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models—0
Active Policy Improvement from Multiple Black-box OraclesCode0
Learning Space-Time Semantic Correspondences—0
Mimicking Better by Matching the Approximate Action DistributionCode0
Residual Q-Learning: Offline and Online Policy Customization without Value—0
Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision TransformersCode1
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving—0
Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided ExplorationCode0
Curricular Subgoals for Inverse Reinforcement LearningCode1
Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer—0
Skill Disentanglement for Imitation Learning from Suboptimal DemonstrationsCode0
Provably Efficient Adversarial Imitation Learning with Unknown TransitionsCode0
Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning—0
PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning—0
Transferring Foundation Models for Generalizable Robotic Manipulation—0
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking—0
Divide and Repair: Using Options to Improve Performance of Imitation Learning Against Adversarial Demonstrations—0
Orca: Progressive Learning from Complex Explanation Traces of GPT-4Code1
Data Quality in Imitation Learning—0
On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control—0
Show:102550
← PrevPage 33 of 85Next →

No leaderboard results yet.