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Papers on “reinforcement learning robotics control”

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1. Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)

   Esper, Khalil, Spieck, Jan, Sixdenier, Pierre-Louis, et al. · 2017 · arXiv (Cornell University) · 11,356 cites

   Due to the safety risks and training sample inefficiency, it is often preferred to develop controllers in simulation. However, minor differences between the simulation and the real world can cause a significant sim-to-real gap. This gap can reduce the effectiveness of the developed controller. In this paper, we examine a case study of transferring an octorotor reinforcement learning controller from simulation to the real world. First, we quantify the effectiveness of the real-world transfer by examining safety metrics. We find that although there is a noticeable (around 100%) increase in deviation in real flights, this deviation may not be considered unsafe, as it will be within > 2m safety

2. Reinforcement Learning: A Survey

   Leslie Pack Kaelbling, Michael L. Littman, Andrew Moore · 1996 · Journal of Artificial Intelligence Research · 8,955 cites

   This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations o

3. Playing Atari with Deep Reinforcement Learning

   Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al. · 2013 · arXiv (Cornell University) · 5,104 cites

   We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.

4. Deep Reinforcement Learning: A Brief Survey

   Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, et al. · 2017 · IEEE Signal Processing Magazine · 4,437 cites

   Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey wil

5. A Comprehensive Survey of Multiagent Reinforcement Learning

   Lucian Buşoniu, Robert Babuška, Bart De Schutter · 2008 · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · 2,259 cites

   Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning. A significant part of the research on multiagent learning concerns reinforcement learning techniques. This paper provides a comprehensive survey of multiagent reinforcement learning (MARL). A central issue in the field is the formal statement of the multiagent learning goal. Different viewpoints on this issue have led to the proposal of many differe

6. Continuous control for robot based on deep reinforcement learning

   Shansi Zhang · 2019 · 951 cites

   One of the main targets of artificial intelligence is to solve the complex control problems which have high-dimensional observation spaces. Recently, the combination of deep learning and reinforcement learning has made remarkable progress, including the high-level performance in the video and board games, 3D navigations and robotic control. In this thesis, deep reinforcement learning algorithms are studied to perform some robotic tasks with continuous action spaces.

7. Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation

   Lei Tai, Giuseppe Paolo, Ming Liu · 2017 · 822 cites

   We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planne

8. Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

   Lukas Brunke, Melissa Greeff, Adam W. Hall, et al. · 2022 · Annual Review of Control Robotics and Autonomous Systems · 746 cites

   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 communities. This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision-making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research. It includes learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforcement learning approaches that encourage safety or robustness, and methods that can formally certify the safet