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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 1226–1250 of 2122 papers

TitleStatusHype
Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)—0
Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction—0
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation—0
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and InvestigationCode0
Learning to Prove Trigonometric Identities—0
Finding Fallen Objects Via Asynchronous Audio-Visual Integration—0
Planning with RL and episodic-memory behavioral priors—0
Learning to Accelerate Approximate Methods for Solving Integer Programming via Early FixingCode0
Discriminator-Guided Model-Based Offline Imitation Learning—0
Watch and Match: Supercharging Imitation with Regularized Optimal Transport—0
Learning energy-efficient driving behaviors by imitating experts—0
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning—0
Improving Policy Optimization with Generalist-Specialist LearningCode0
Auto-Encoding Adversarial Imitation Learning—0
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real TransferCode0
Latent Policies for Adversarial Imitation Learning—0
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—0
Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO—0
Model-Based Imitation Learning Using Entropy Regularization of Model and Policy—0
Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Case-Based Inverse Reinforcement Learning Using Temporal CoherenceCode0
Model-based Offline Imitation Learning with Non-expert Data—0
Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning—0
Precise Affordance Annotation for Egocentric Action Video Datasets—0
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