End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning

Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems

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Challenges of real-world reinforcement learning: definitions, benchmarks and analysis

Machine Learning ◽

10.1007/s10994-021-05961-4 ◽

2021 ◽

Author(s):

Gabriel Dulac-Arnold ◽

Nir Levine ◽

Daniel J. Mankowitz ◽

Jerry Li ◽

Cosmin Paduraru ◽

...

Keyword(s):

Reinforcement Learning ◽

Real World

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Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

Machine Learning ◽

10.1007/s10994-020-05939-8 ◽

2021 ◽

Author(s):

Amarildo Likmeta ◽

Alberto Maria Metelli ◽

Giorgia Ramponi ◽

Andrea Tirinzoni ◽

Matteo Giuliani ◽

...

Keyword(s):

Reinforcement Learning ◽

Real World ◽

Real Life ◽

User Preferences ◽

Inverse Reinforcement Learning ◽

Water Release ◽

Reward Function ◽

Model Free ◽

Conflicting Objectives ◽

Multiple Experts

AbstractIn real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

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Verifiably safe exploration for end-to-end reinforcement learning

Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control ◽

10.1145/3447928.3456653 ◽

2021 ◽

Author(s):

Nathan Hunt ◽

Nathan Fulton ◽

Sara Magliacane ◽

Trong Nghia Hoang ◽

Subhro Das ◽

...

Keyword(s):

Reinforcement Learning ◽

End To End

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End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding

Proceedings of the 13th International Conference on Web Search and Data Mining ◽

10.1145/3336191.3371858 ◽

2020 ◽

Cited By ~ 1

Author(s):

Feng Liu ◽

Huifeng Guo ◽

Xutao Li ◽

Ruiming Tang ◽

Yunming Ye ◽

...

Keyword(s):

Reinforcement Learning ◽

End To End

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Goal-driven active learning

Autonomous Agents and Multi-Agent Systems ◽

10.1007/s10458-021-09527-5 ◽

2021 ◽

Vol 35 (2) ◽

Author(s):

Nicolas Bougie ◽

Ryutaro Ichise

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Learning Process ◽

Real World ◽

Imitation Learning ◽

Learning Approaches ◽

Wide Range ◽

Fixed Set ◽

Complex Decision Making ◽

Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

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How to train your robot with deep reinforcement learning: lessons we have learned

The International Journal of Robotics Research ◽

10.1177/0278364920987859 ◽

2021 ◽

pp. 027836492098785

Author(s):

Julian Ibarz ◽

Jie Tan ◽

Chelsea Finn ◽

Mrinal Kalakrishnan ◽

Peter Pastor ◽

...

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Case Studies ◽

Real World ◽

Review Article ◽

The Real ◽

Complex Skills ◽

Real World Learning ◽

Level Sensor ◽

Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

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