Safe learning in robotics: From learning-based control to safe reinforcement learning
The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …
methods for real-world robotic deployments from both the control and reinforcement learning …
Robust reinforcement learning: A review of foundations and recent advances
Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Rambo-rl: Robust adversarial model-based offline reinforcement learning
Offline reinforcement learning (RL) aims to find performant policies from logged data without
further environment interaction. Model-based algorithms, which learn a model of the …
further environment interaction. Model-based algorithms, which learn a model of the …
Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning
The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
How simulation helps autonomous driving: A survey of sim2real, digital twins, and parallel intelligence
Developing autonomous driving technologies necessitates addressing safety and cost
concerns. Both academic research and commercial applications of autonomous driving …
concerns. Both academic research and commercial applications of autonomous driving …
One solution is not all you need: Few-shot extrapolation via structured maxent rl
While reinforcement learning algorithms can learn effective policies for complex tasks, these
policies are often brittle to even minor task variations, especially when variations are not …
policies are often brittle to even minor task variations, especially when variations are not …
Challenges and countermeasures for adversarial attacks on deep reinforcement learning
Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to
its ability to achieve high performance in a range of environments with little manual …
its ability to achieve high performance in a range of environments with little manual …
A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning
This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
Robust multi-agent reinforcement learning via adversarial regularization: Theoretical foundation and stable algorithms
Abstract Multi-Agent Reinforcement Learning (MARL) has shown promising results across
several domains. Despite this promise, MARL policies often lack robustness and are …
several domains. Despite this promise, MARL policies often lack robustness and are …
Risk-averse offline reinforcement learning
Training Reinforcement Learning (RL) agents in high-stakes applications might be too
prohibitive due to the risk associated to exploration. Thus, the agent can only use data …
prohibitive due to the risk associated to exploration. Thus, the agent can only use data …