21 research outputs found

    Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

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    This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domainā€™s complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks

    Safe Reinforcement Learning with Contrastive Risk Prediction

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    As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL). In this work, we propose a risk preventive training method for safe RL, which learns a statistical contrastive classifier to predict the probability of a state-action pair leading to unsafe states. Based on the predicted risk probabilities, we can collect risk preventive trajectories and reshape the reward function with risk penalties to induce safe RL policies. We conduct experiments in robotic simulation environments. The results show the proposed approach has comparable performance with the state-of-the-art model-based methods and outperforms conventional model-free safe RL approaches

    Prediction of On-Disk Velocity Across a Coaxial Rotor with XGBoost

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    Recent updates in ļ¬nite state inļ¬‚ow models to solve multi-rotor systems has come at the expense of extra computation time requirements, especially for higher harmonic cases. A potential solution to counter the lengthy time requirements is the application of machine learning algorithms to ļ¬t to velocity distributions and predict future distributions. In this paper, we look at XGBoost as a potential application of machine learning to predict accurate velocity distributions across the rotor disk

    Do Artificial Reinforcement-Learning Agents Matter Morally?

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    Artificial reinforcement learning (RL) is a widely used technique in artificial intelligence that provides a general method for training agents to perform a wide variety of behaviours. RL as used in computer science has striking parallels to reward and punishment learning in animal and human brains. I argue that present-day artificial RL agents have a very small but nonzero degree of ethical importance. This is particularly plausible for views according to which sentience comes in degrees based on the abilities and complexities of minds, but even binary views on consciousness should assign nonzero probability to RL programs having morally relevant experiences. While RL programs are not a top ethical priority today, they may become more significant in the coming decades as RL is increasingly applied to industry, robotics, video games, and other areas. I encourage scientists, philosophers, and citizens to begin a conversation about our ethical duties to reduce the harm that we inflict on powerless, voiceless RL agents.Comment: 37 page
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