Toward Self-Driving Bicycles Using State-of-the-Art Deep Reinforcement Learning Algorithms

Choi, Seung-yoon; Le, Tuyen P.; Nguyen, Quang; Layek, Md. Abu; Lee, SeungGwan; Chung, T. J.

doi:10.3390/sym11020290

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…If our agent defeats the opponents with a better probability, the output of the agent is required to be more stable in the robot confrontation system for micromanagement. The classical Q-learning method solves the dilemma of exploration and exploitation using the ε-greedy algorithm, which makes the agent have a certain probability to explore new actions [28]. However, the probability of each action being selected is the same when the ε-greedy algorithm is used, so the action that can produce better rewards is not easy to choose.…”

Section: An Improved Q-learning Methods In Semi-markov Decision Processesmentioning

confidence: 99%

Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Meng

2019

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Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Qlearning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.

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Section: An Improved Q-learning Methods In Semi-markov Decision Processesmentioning

confidence: 99%

Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Meng

2019

Information

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“…6. The decision-maker is configured as a neural network [82][83][84][85] that can make decisions on the SD compliance levels (within the limit SD max ), given the decision-maker's observation of the environment. The environment comprises the ABM which simulates effects of these decisions on the transmission and control of the COVID-19 within a typical Australian town, as described in previous section.…”

Section: Agent-based Model For Covid-19 Transmission and Controlmentioning

confidence: 99%

A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures

Nguyen

Prokopenko

2022

Sci Rep

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The COVID-19 pandemic created enormous public health and socioeconomic challenges. The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often contrasted with significant social and economic costs. We describe a general framework aimed to derive adaptive cost-effective interventions, adequate for both recent and emerging pandemic threats. We also quantify the net health benefits and propose a reinforcement learning approach to optimise adaptive NPIs. The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogeneous population with variable levels of compliance fluctuating over time and across individuals. Our analysis shows that a significant net health benefit may be attained by adaptive NPIs formed by partial social distancing measures, coupled with moderate levels of the society’s willingness to pay for health gains (health losses averted). We demonstrate that a socially acceptable balance between health effects and incurred economic costs is achievable over a long term, despite possible early setbacks.

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“…In recent years, reinforcement learning has been widely used in the field of robot control. Choi et al (2019) took the lead in using DDPG to realize the balance control of STTW robot. However, this paper did not consider the task of robot passing through unstructured terrain, which will be a focus of our work.…”

Section: Preliminariesmentioning

confidence: 99%

Continuous reinforcement learning based ramp jump control for single-track two-wheeled robots

Zheng

Wang

Chen

et al. 2021

Transactions of the Institute of Measurement and Control

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Single-track two-wheeled robots have become an important research topic in recent years, owing to their simple structure, energy savings and ability to run on narrow roads. However, the ramp jump remains a challenging task. In this study, we propose to realize a single-track two-wheeled robot ramp jump. We present a control method that employs continuous action reinforcement learning techniques for single-track two-wheeled robot control. We design a novel reward function for reinforcement learning, optimize the dimensions of the action space, and enable training under the deep deterministic policy gradient algorithm. Finally, we validate the control method through simulation experiments and successfully realize the single-track two-wheeled robot ramp jump task. Simulation results validate that the control method is effective and has several advantages over high-dimension action space control, reinforcement learning control of sparse reward function and discrete action reinforcement learning control.

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Toward Self-Driving Bicycles Using State-of-the-Art Deep Reinforcement Learning Algorithms

Cited by 19 publications

References 11 publications

Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures

Continuous reinforcement learning based ramp jump control for single-track two-wheeled robots

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