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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 1351–1375 of 2122 papers

TitleStatusHype
Transferring Hierarchical Structure with Dual Meta Imitation Learning—0
Transfer RL via the Undo Maps Formalism—0
Transformer-based deep imitation learning for dual-arm robot manipulation—0
Transformers for One-Shot Visual Imitation—0
Translating Natural Language Instructions to Computer Programs for Robot Manipulation—0
Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks—0
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets—0
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning—0
Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs—0
UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation—0
UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning—0
UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning—0
UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound—0
Unbiased learning with State-Conditioned Rewards in Adversarial Imitation Learning—0
Uncertainty-Aware Data Aggregation for Deep Imitation Learning—0
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives—0
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations—0
Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis—0
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective—0
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process—0
Understanding Representations Pretrained with Auxiliary Losses for Embodied Agent Planning—0
Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View—0
Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets—0
Unpacking the Individual Components of Diffusion Policy—0
Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer—0
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