RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning
While Adversarial Imitation Learning (AIL) algorithms have recently led to state-of-the-art results on various imitation learning benchmarks, it is unclear as to what impact various design decisions have on performance. To this end, we present here an organizing, modular framework called Reinforcement-learning-based Adversarial Imitation Learning (RAIL) that encompasses and generalizes a popular subclass of existing AIL approaches. Using the view espoused by RAIL, we create two new IfO (Imitation from Observation) algorithms, which we term SAIfO: SAC-based Adversarial Imitation from Observation and SILEM (Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch). We go into greater depth about SILEM in a separate technical report. In this paper, we focus on SAIfO, evaluating it on a suite of locomotion tasks from OpenAI Gym, and showing that it outperforms contemporaneous RAIL algorithms that perform IfO.
Code (0)
등록된 구현이 없습니다.
Tasks
Imitation LearningOpenAI Gymreinforcement-learningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Diffusion-Reward Adversarial Imitation Learning
Imitation learning aims to learn a policy from observing expert demonstrations without access to reward signals from environments. Generative adversarial imitation learning (GAIL) formulates imitation learning as adversa…
Imitation LearningTask-Relevant Adversarial Imitation Learning
We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels. When the discriminator focuses on task-ir…
Imitation LearningContrail-to-Flight Attribution Using Ground Visible Cameras and Flight Surveillance Data
Aviation's non-CO2 effects, particularly contrails, are a significant contributor to its climate impact. Persistent contrails can evolve into cirrus-like clouds that trap outgoing infrared radiation, with radiative forci…
Lightweight Safety Guardrails via Synthetic Data and RL-guided Adversarial Training
We introduce a lightweight yet highly effective safety guardrail framework for language models, demonstrating that small-scale language models can achieve, and even surpass, the performance of larger counterparts in cont…
Generative Adversarial NetworkSynthetic Data GenerationRAIL: Risk-Averse Imitation Learning
Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning p…
Autonomous Drivingcontinuous-controlContinuous ControlImitation Learning