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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 1576–1600 of 2122 papers

TitleStatusHype
Offline Imitation Learning with a Misspecified Simulator—0
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning—0
Bayesian Multi-type Mean Field Multi-agent Imitation Learning—0
Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Human-Agent Cooperation in Bridge Bidding—0
Offline Learning from Demonstrations and Unlabeled Experience—0
Episodic Self-Imitation Learning with HindsightCode0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning—0
SAFARI: Safe and Active Robot Imitation Learning with Imagination—0
Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation—0
Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning—0
Transformers for One-Shot Visual Imitation—0
Safe Trajectory Planning Using Reinforcement Learning for Self Driving—0
HILONet: Hierarchical Imitation Learning from Non-Aligned Observations—0
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control—0
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models—0
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving—0
Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication—0
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT—0
Fighting Copycat Agents in Behavioral Cloning from Observation Histories—0
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning—0
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills—0
Complex Skill Acquisition through Simple Skill Imitation Learning—0
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