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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 251–275 of 2122 papers

TitleStatusHype
FDPP: Fine-tune Diffusion Policy with Human Preference—0
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline DataCode0
Generalizable Graph Neural Networks for Robust Power Grid Topology ControlCode0
Motion Tracks: A Unified Representation for Human-Robot Transfer in Few-Shot Imitation Learning—0
Smart Imitator: Learning from Imperfect Clinical DecisionsCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification—0
SR-Reward: Taking The Path More Traveled—0
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving—0
Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency—0
Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors—0
The intrinsic motivation of reinforcement and imitation learning for sequential tasks—0
Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action RankerCode0
Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene Interaction Learning via Human Mimicking—0
Decoding fairness: a reinforcement learning perspectiveCode0
SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branch—0
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination—0
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models—0
Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model—0
RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation—0
Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask LearningCode2
Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)—0
Student-Informed Teacher Training—0
Learning Novel Skills from Language-Generated Demonstrations—0
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