Imitation Learning
520 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
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
This work offers a hands-on reference for the research progress in deep generative models for offline policy learning, and aims to inspire improved DGM-based offline RL or IL algorithms.
Tiny Reinforcement Learning for Quadruped Locomotion using Decision Transformers
Our results show that quantization (down to 4 bits) and pruning reduce model size by around 30\% while maintaining a competitive reward, making the model deployable in a resource-constrained system.
PRISE: Learning Temporal Action Abstractions as a Sequence Compression Problem
To do so, we bring a subtle but critical component of LLM training pipelines -- input tokenization via byte pair encoding (BPE) -- to the seemingly distant task of learning skills of variable time span in continuous control domains.
Hybrid Inverse Reinforcement Learning
jren03/garage
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In this work, we propose using hybrid RL -- training on a mixture of online and expert data -- to curtail unnecessary exploration.
A Competition Winning Deep Reinforcement Learning Agent in microRTS
This first competition-winning DRL submission can be a benchmark for future microRTS competitions and a starting point for future DRL research.
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.
SEABO: A Simple Search-Based Method for Offline Imitation Learning
dmksjfl/seabo
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Offline reinforcement learning (RL) has attracted much attention due to its ability in learning from static offline datasets and eliminating the need of interacting with the environment.
AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies
hxixixh/adaflow
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To address this challenge, we propose AdaFlow, an imitation learning framework based on flow-based generative modeling.
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
To resolve this issue, we propose a simple yet effective modification that projects the backward gradient onto the normal plane of the forward gradient, resulting in an orthogonal-gradient update, a new learning rule for DICE-based methods.
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning
stanl1y/tdil
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In this paper, we focus on single-demonstration imitation learning (IL), a practical approach for real-world applications where obtaining numerous expert demonstrations is costly or infeasible.
