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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 1526–1550 of 2122 papers

TitleStatusHype
Safe Trajectory Planning Using Reinforcement Learning for Self Driving—0
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement LearningCode1
HILONet: Hierarchical Imitation Learning from Non-Aligned Observations—0
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models—0
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control—0
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT—0
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving—0
The MAGICAL Benchmark for Robust ImitationCode1
Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication—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
Language-Conditioned Imitation Learning for Robot Manipulation TasksCode1
Error Bounds of Imitating Policies and Environments—0
Robust Imitation Learning from Noisy DemonstrationsCode0
Imitation with Neural Density Models—0
Training Stronger Baselines for Learning to OptimizeCode1
Learning to Select Nodes in Bounded Suboptimal Conflict-Based Search for Multi-Agent Path Finding—0
On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration—0
Self-Imitation Learning for Robot Tasks with Sparse and Delayed RewardsCode0
Deep Imitation Learning for Bimanual Robotic ManipulationCode1
Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation LearningCode1
LaND: Learning to Navigate from DisengagementsCode1
Provable Hierarchical Imitation Learning via EMCode0
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