A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations
This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations.
Code (1)
Tasks
continuous-controlContinuous ControlDeep Reinforcement Learningreinforcement-learningReinforcement LearningSimilar Papers 제목 키워드 기반
Adversarial Imitation Learning from Visual Observations using Latent Information
We focus on the problem of imitation learning from visual observations, where the learning agent has access to videos of experts as its sole learning source. The challenges of this framework include the absence of expert…
Imitation LearningInverse Delayed Reinforcement Learning
Inverse Reinforcement Learning (IRL) has demonstrated effectiveness in a variety of imitation tasks. In this paper, we introduce an IRL framework designed to extract rewarding features from expert trajectories affected b…
MuJoCoreinforcement-learningReinforcement LearningSeMAIL: Eliminating Distractors in Visual Imitation via Separated Models
Model-based imitation learning (MBIL) is a popular reinforcement learning method that improves sample efficiency on high-dimension input sources, such as images and videos. Following the convention of MBIL research, exis…
Imitation LearningComposition of Memory Experts for Diffusion World Models
World models aim to predict plausible futures consistent with past observations, a capability central to planning and decision-making in reinforcement learning. Yet, existing architectures face a fundamental memory trade…
Reinforcement LearningSample Efficient Social Navigation Using Inverse Reinforcement Learning
In this paper, we present an algorithm to efficiently learn socially-compliant navigation policies from observations of human trajectories. As mobile robots come to inhabit and traffic social spaces, they must account fo…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Social Navigation