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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 1151–1200 of 2122 papers

TitleStatusHype
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies—0
SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies—0
Saliency Prediction on Omnidirectional Images with Generative Adversarial Imitation Learning—0
Sample-efficient Adversarial Imitation Learning from Observation—0
Sample-efficient Adversarial Imitation Learning—0
Sample Efficient Imitation Learning for Continuous Control—0
Sample Efficient Imitation Learning via Reward Function Trained in Advance—0
Physics-informed Imitative Reinforcement Learning for Real-world Driving—0
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation—0
Sample Efficient Learning of Path Following and Obstacle Avoidance Behavior for Quadrotors—0
Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication—0
Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward—0
Scalable Multi-Task Imitation Learning with Autonomous Improvement—0
Scaling Laws for Pre-training Agents and World Models—0
Scaling Vision-based End-to-End Driving with Multi-View Attention Learning—0
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning—0
SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy—0
SDA: Improving Text Generation with Self Data Augmentation—0
SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey—0
SEAL: SEmantic-Augmented Imitation Learning via Language Model—0
Searching for Objects using Structure in Indoor Scenes—0
Seeded self-play for language learning—0
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning—0
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition—0
Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games—0
Selective Sampling and Imitation Learning via Online Regression—0
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning—0
Self-Imitation Advantage Learning—0
Self-Imitation Learning by Planning—0
Self-Imitation Learning from Demonstrations—0
Self-Imitation Learning via Generalized Lower Bound Q-learning—0
Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks—0
Self-Motivated Communication Agent for Real-World Vision-Dialog Navigation—0
Self-reconfiguration Strategies for Space-distributed Spacecraft—0
Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency—0
Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning—0
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models—0
Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations—0
Semi-Supervised One-Shot Imitation Learning—0
SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring—0
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking—0
Sequential Causal Imitation Learning with Unobserved Confounders—0
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models—0
Shared Multi-Task Imitation Learning for Indoor Self-Navigation—0
SHEF-MIME: Word-level Quality Estimation Using Imitation Learning—0
SIMILE: Introducing Sequential Information towards More Effective Imitation Learning—0
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey—0
Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup—0
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