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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 576–600 of 2122 papers

TitleStatusHype
Auto-Encoding Adversarial Imitation Learning—0
Convergence of Value Aggregation for Imitation Learning—0
Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)—0
Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors—0
Extraneousness-Aware Imitation Learning—0
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation—0
A Unifying Framework for Causal Imitation Learning with Hidden Confounders—0
Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories—0
Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning—0
Continuous Online Learning and New Insights to Online Imitation Learning—0
Accelerating Imitation Learning with Predictive Models—0
Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)—0
Continuous Control with Action Quantization from Demonstrations—0
Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling—0
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills—0
Augmented Reality Demonstrations for Scalable Robot Imitation Learning—0
Affordances from Human Videos as a Versatile Representation for Robotics—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Fast fixed-backbone protein sequence and rotamer design—0
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning—0
Contextual Bandits and Imitation Learning via Preference-Based Active Queries—0
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation—0
Context-Former: Stitching via Latent Conditioned Sequence Modeling—0
Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline—0
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