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Found 8 papers, 6 papers with code
Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems
Unfortunately, both performance and solution feasibility can be significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness.
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.
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.
A Reinforcement Learning Approach for Robotic Unloading from Visual Observations
In this work, we focus on a robotic unloading problem from visual observations, where robots are required to autonomously unload stacks of parcels using RGB-D images as their primary input source.
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation
We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards.
Unsupervised Reward Shaping for a Robotic Sequential Picking Task from Visual Observations in a Logistics Scenario
We focus on an unloading problem, typical of the logistics sector, modeled as a sequential pick-and-place task.
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task
We show that the combination of IL and RL can match human results and that good performance strongly depends on combining the allocentric information with an egocentric representation of the environment.
Online Baum-Welch algorithm for Hierarchical Imitation Learning
This problem is referred to as hierarchical imitation learning and can be handled as an inference problem in a Hidden Markov Model, which is done via an Expectation-Maximization type algorithm.
