Imitation Learning
519 papers with code • 0 benchmarks • 18 datasets
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
Benchmarks
These leaderboards are used to track progress in Imitation Learning
Libraries
Use these libraries to find Imitation Learning models and implementationsDatasets
CARLA
AirSim
AI2-THOR
TORCS
RLBench
INTERACTION Dataset
ManiSkill2
CoDraw
CHALET
CubiCasa5K
Latest papers with no code
Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent Collaboration
Initializing policies to maximize performance with unknown partners can be achieved by bootstrapping nonlinear models using imitation learning over large, offline datasets.
Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model
Many recent studies endeavor to improve open-source language models through imitation learning, and re-training on the synthetic instruction data from state-of-the-art proprietary models like ChatGPT and GPT-4.
Adversarial Imitation Learning via Boosting
In the weighted replay buffer, the contribution of the data from older policies are properly discounted with the weight computed based on the boosting framework.
AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent
Encouraged by the remarkable achievements of language and vision foundation models, developing generalist robotic agents through imitation learning, using large demonstration datasets, has become a prominent area of interest in robot learning.
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery
This paper introduces a sample-efficient method that learns a robust reward function from a limited amount of ranked suboptimal demonstrations consisting of partial-view point cloud observations.
CNN-based Game State Detection for a Foosball Table
In the game of Foosball, a compact and comprehensive game state description consists of the positional shifts and rotations of the figures and the position of the ball over time.
SAFE-GIL: SAFEty Guided Imitation Learning
The algorithm abstracts the imitation error as an adversarial disturbance in the system dynamics, injects it during data collection to expose the expert to safety critical states, and collects corrective actions.
Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs
The utilization of Large Language Models (LLMs) within the realm of reinforcement learning, particularly as planners, has garnered a significant degree of attention in recent scholarly literature.
SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring
In many real-world visual Imitation Learning (IL) scenarios, there is a misalignment between the agent's and the expert's perspectives, which might lead to the failure of imitation.
DIDA: Denoised Imitation Learning based on Domain Adaptation
Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications.
