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 976–1000 of 2122 papers

TitleStatusHype
CASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters—0
Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving—0
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions—0
Naturalistic Robot Arm Trajectory Generation via Representation Learning—0
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for End-to-End Autonomous Driving—0
Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets—0
A Reinforcement Learning Approach for Robotic Unloading from Visual ObservationsCode0
Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions—0
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation—0
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges—0
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning—0
Enhancing Robot Learning through Learned Human-Attention Feature MapsCode0
End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data—0
Conditional Kernel Imitation Learning for Continuous State Environments—0
Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games—0
ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration—0
Preference-conditioned Pixel-based AI Agent For Game Testing—0
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making—0
Regularizing Adversarial Imitation Learning Using Causal Invariance—0
Leveraging Symmetries in Pick and Place—0
Generating Personas for Games with Multimodal Adversarial Imitation Learning—0
Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs—0
MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation—0
Cognitive TransFuser: Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint PredictionCode0
Initial State Interventions for Deconfounded Imitation Learning—0
Show:102550
← PrevPage 40 of 85Next →

No leaderboard results yet.