Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning
In this work, we present a method for obtaining an implicit objective function for vision-based navigation. The proposed methodology relies on Imitation Learning, Model Predictive Control (MPC), and an interpretation technique used in Deep Neural Networks. We use Imitation Learning as a means to do Inverse Reinforcement Learning in order to create an approximate cost function generator for a visual navigation challenge. The resulting cost function, the costmap, is used in conjunction with MPC for real-time control and outperforms other state-of-the-art costmap generators in novel environments. The proposed process allows for simple training and robustness to out-of-sample data. We apply our method to the task of vision-based autonomous driving in multiple real and simulated environments and show its generalizability.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingImitation LearningModel Predictive Controlreinforcement-learningReinforcement LearningReinforcement Learning (RL)Visual NavigationSimilar Papers 제목 키워드 기반
Scalable Bayesian Inverse Reinforcement Learning
Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not scale well beyond the small tabular sett…
Bayesian InferenceImitation Learningreinforcement-learningReinforcement Learning+1Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward
Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not scale well beyond the small tabular sett…
Bayesian InferenceImitation Learningreinforcement-learningReinforcement Learning+1Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment
The alignment of large language models (LLMs) is crucial for generating helpful and harmless content. Existing approaches leverage preference-based human feedback data to learn the reward function and align the LLM with …
BIRLImitation LearningLanguage ModelingLanguage Modelling+2Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a reward function that makes the demonstrator …
Deep Reinforcement LearningImitation LearningMuJoCoreinforcement-learning+2A proof of imitation of Wasserstein inverse reinforcement learning for multi-objective optimization
We prove Wasserstein inverse reinforcement learning enables the learner's reward values to imitate the expert's reward values in a finite iteration for multi-objective optimizations. Moreover, we prove Wasserstein invers…
reinforcement-learningReinforcement Learning