Imitation Learning
521 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
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Libraries
Use these libraries to find Imitation Learning models and implementationsDatasets
Latest papers
LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization
This paper introduces LeTO, a method for learning constrained visuomotor policy via differentiable trajectory optimization.
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning
This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news.
States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers
A suitable model of the players, strategies, and payoffs associated with linguistic interactions (i. e., a binding to the conventional symbolic logic of game theory) would enable existing game-theoretic algorithms to provide strategic solutions in the space of language.
LangProp: A code optimization framework using Language Models applied to driving
LangProp is a framework for iteratively optimizing code generated by large language models (LLMs) in a supervised/reinforcement learning setting.
LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control
Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known a priori.
SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset
In particular, this paper aims to improve the Travel Assist feature for automatic overtaking and lane changes on highways.
LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop Environments
Instructing a robot to complete an everyday task within our homes has been a long-standing challenge for robotics.
DiffAIL: Diffusion Adversarial Imitation Learning
To address this issue, we propose a method named diffusion adversarial imitation learning (DiffAIL), which introduces the diffusion model into the AIL framework.
Backward Learning for Goal-Conditioned Policies
Can we learn policies in reinforcement learning without rewards?
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld
While large language models (LLMs) excel in a simulated world of texts, they struggle to interact with the more realistic world without perceptions of other modalities such as visual or audio signals.