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Found 7 papers, 4 papers with code
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.
Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees
Robustness and safety are critical for the trustworthy deployment of deep reinforcement learning.
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.
Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
Data-driven, learning-based control methods offer the potential to improve operations in complex systems, and model-free deep reinforcement learning represents a popular approach to data-driven control.
Generalized Proximal Policy Optimization with Sample Reuse
In real-world decision making tasks, it is critical for data-driven reinforcement learning methods to be both stable and sample efficient.
Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach
In order for reinforcement learning techniques to be useful in real-world decision making processes, they must be able to produce robust performance from limited data.
