Imitation Learning
522 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 with no code
LORD: Large Models based Opposite Reward Design for Autonomous Driving
Recently, large pretrained models have gained significant attention as zero-shot reward models for tasks specified with desired linguistic goals.
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation
Due to the covariate shift issue, existing imitation learning-based simulators often fail to generate stable long-term simulations.
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations
Grounding the common-sense reasoning of Large Language Models (LLMs) in physical domains remains a pivotal yet unsolved problem for embodied AI.
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination
In our new Dynamic Learning from Learned Hallucination (Dyna-LfLH), we design and learn a novel latent distribution and sample dynamic obstacles from it, so the generated training data can be used to learn a motion planner to navigate in dynamic environments.
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
In this work, we present a framework called IRL (Interpretable Reinforcement Learning) to address the issue of interpretability of DRL scheduling.
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History
This poses a new challenge for existing imitation learning approaches that can only utilize data from experienced experts.
Automated Feature Selection for Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations.
Information-Theoretic Distillation for Reference-less Summarization
The current winning recipe for automatic summarization is using proprietary large-scale language models (LLMs) such as ChatGPT as is, or imitation learning from them as teacher models.
Augmented Reality Demonstrations for Scalable Robot Imitation Learning
Our framework facilitates scalable and diverse demonstration collection for real-world tasks.
Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types
Notably, our model, trained solely on data from a transparent glass bowl containing granular cereals, showcases generalization ability when tested zero-shot on other bowl configurations with different types of food.