scholarly journals Decision-making method for vehicle longitudinal automatic driving based on reinforcement Q-learning

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141985318
Author(s):  
Zhenhai Gao ◽  
Tianjun Sun ◽  
Hongwei Xiao
Keyword(s):  
Decision Making ◽  
Process Model ◽  
Driving Simulator ◽  
Learning Algorithm ◽  
Autonomous Driving ◽  
Sequential Decision ◽  
Q Learning ◽  
Reward Function ◽  
Automatic Driving ◽  
Independent Decision

In the development of autonomous driving, decision-making has become one of the technical difficulties. Traditional rule-based decision-making methods lack adaptive capacity when dealing with unfamiliar and complex traffic conditions. However, reinforcement learning shows the potential to solve sequential decision problems. In this article, an independent decision-making method based on reinforcement Q-learning is proposed. First, a Markov decision process model is established by analysis of car-following. Then, the state set and action set are designed by the synthesized consideration of driving simulator experimental results and driving risk principles. Furthermore, the reinforcement Q-learning algorithm is developed mainly based on the reward function and update function. Finally, the feasibility is verified through random simulation tests, and the improvement is made by comparative analysis with a traditional method.

scholarly journals Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881716 ◽  
Author(s):  
Hongbo Gao ◽  
Guanya Shi ◽  
Guotao Xie ◽  
Bo Cheng
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Sequential Decision ◽  
Inverse Reinforcement Learning ◽  
Car Following ◽  
Reward Function ◽  
Automatic Driving

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment.

Driver-like decision-making method for vehicle longitudinal autonomous driving based on deep reinforcement learning

2021 ◽  
pp. 095440702110630
Author(s):  
Zhenhai Gao ◽  
Xiangtong Yan ◽  
Fei Gao ◽  
Lei He
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Autonomous Driving ◽  
Decision Strategies ◽  
Reward Function ◽  
Human Driver ◽  
Reward Functions ◽  
A Current ◽  
Better Than

Decision-making is one of the key parts of the research on vehicle longitudinal autonomous driving. Considering the behavior of human drivers when designing autonomous driving decision-making strategies is a current research hotspot. In longitudinal autonomous driving decision-making strategies, traditional rule-based decision-making strategies are difficult to apply to complex scenarios. Current decision-making methods that use reinforcement learning and deep reinforcement learning construct reward functions designed with safety, comfort, and economy. Compared with human drivers, the obtained decision strategies still have big gaps. Focusing on the above problems, this paper uses the driver’s behavior data to design the reward function of the deep reinforcement learning algorithm through BP neural network fitting, and uses the deep reinforcement learning DQN algorithm and the DDPG algorithm to establish two driver-like longitudinal autonomous driving decision-making models. The simulation experiment compares the decision-making effect of the two models with the driver curve. The results shows that the two algorithms can realize driver-like decision-making, and the consistency of the DDPG algorithm and human driver behavior is higher than that of the DQN algorithm, the effect of the DDPG algorithm is better than the DQN algorithm.

scholarly journals Decision-Making for the Autonomous Navigation of Maritime Autonomous Surface Ships Based on Scene Division and Deep Reinforcement Learning

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4055 ◽  
Author(s):  
Zhang ◽  
Wang ◽  
Liu ◽  
Chen
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Learning Algorithm ◽  
Q Learning ◽  
Reward Function ◽  
International Regulations ◽  
Convergence Trend ◽  
Decision Making Model

This research focuses on the adaptive navigation of maritime autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is mainly composed of two layers: the scene division layer and an autonomous navigation decision-making layer. The scene division layer mainly quantifies the sub-scenarios according to the International Regulations for Preventing Collisions at Sea (COLREG). This research divides the navigational situation of a ship into entities and attributes based on the ontology model and Protégé language. In the decision-making layer, we designed a deep Q-learning algorithm utilizing the environmental model, ship motion space, reward function, and search strategy to learn the environmental state in a quantized sub-scenario to train the navigation strategy. Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. Moreover, the experimental data were analyzed in terms of the convergence trend, iterative path, and collision avoidance effect. The results indicate that the improved DRL algorithm could effectively improve the navigation safety and collision avoidance.

scholarly journals Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Weilong Song ◽  
Guangming Xiong ◽  
Huiyan Chen
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Transition Process ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Time Step ◽  
Reward Function ◽  
Pass Through

Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently.

scholarly journals Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 737
Author(s):  
Fengjie Sun ◽  
Xianchang Wang ◽  
Rui Zhang
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimal Policy ◽  
Feasible Solution ◽  
Learning Algorithm ◽  
Plant Protection ◽  
Agricultural Plant ◽  
Q Learning ◽  
Aerial Vehicle ◽  
Optimal Action

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.

Safety-aware Adversarial Inverse Reinforcement Learning (S-AIRL) for Highway Autonomous Driving

2022 ◽  
pp. 1-14
Author(s):  
Fangjian Li ◽  
John R Wagner ◽  
Yue Wang
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Risky Behaviors ◽  
Autonomous Driving ◽  
Inverse Reinforcement Learning ◽  
Safety Issues ◽  
Reward Function ◽  
Sampling Process ◽  
Safety Awareness ◽  
Driving Scenario

Abstract Inverse reinforcement learning (IRL) has been successfully applied in many robotics and autonomous driving studies without the need for hand-tuning a reward function. However, it suffers from safety issues. Compared to the reinforcement learning (RL) algorithms, IRL is even more vulnerable to unsafe situations as it can only infer the importance of safety based on expert demonstrations. In this paper, we propose a safety-aware adversarial inverse reinforcement learning algorithm (S-AIRL). First, the control barrier function (CBF) is used to guide the training of a safety critic, which leverages the knowledge of system dynamics in the sampling process without training an additional guiding policy. The trained safety critic is then integrated into the discriminator to help discern the generated data and expert demonstrations from the standpoint of safety. Finally, to further improve the safety awareness, a regulator is introduced in the loss function of the discriminator training to prevent the recovered reward function from assigning high rewards to the risky behaviors. We tested our S-AIRL in the highway autonomous driving scenario. Comparing to the original AIRL algorithm, with the same level of imitation learning (IL) performance, the proposed S-AIRL can reduce the collision rate by 32.6%.

A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms

1999 ◽  
Vol 11 (8) ◽  
pp. 2017-2060 ◽  
Author(s):  
Csaba Szepesvári ◽  
Michael L. Littman
Keyword(s):  
Reinforcement Learning ◽  
Value Function ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Sequential Decision ◽  
Q Learning ◽  
Markov Games ◽  
Optimal Behavior ◽  
Risk Sensitive ◽  
Optimal Value

Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the convergence of a complex asynchronous reinforcement-learning algorithm to be proved by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multistate updates, Q-learning for Markov games, and risk-sensitive reinforcement learning.

scholarly journals Preventing Disparate Treatment in Sequential Decision Making

2018 ◽  
Author(s):  
Hoda Heidari ◽  
Andreas Krause
Keyword(s):  
Decision Making ◽  
Learning Algorithm ◽  
Feature Space ◽  
Sequential Decision Making ◽  
Data Sets ◽  
Sequential Decision ◽  
Real World Data ◽  
Time Step ◽  
Job Application ◽  
Disparate Treatment

We study fairness in sequential decision making environments, where at each time step a learning algorithm receives data corresponding to a new individual (e.g. a new job application) and must make an irrevocable decision about him/her (e.g. whether to hire the applicant) based on observations made so far. In order to prevent cases of disparate treatment, our time-dependent notion of fairness requires algorithmic decisions to be consistent: if two individuals are similar in the feature space and arrive during the same time epoch, the algorithm must assign them to similar outcomes. We propose a general framework for post-processing predictions made by a black-box learning model, that guarantees the resulting sequence of outcomes is consistent. We show theoretically that imposing consistency will not significantly slow down learning. Our experiments on two real-world data sets illustrate and confirm this finding in practice.

scholarly journals Cognitive Electronic Jamming Decision-Making Method Based on Improved Q -Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huiqin Li ◽  
Yanling Li ◽  
Chuan He ◽  
Jianwei Zhan ◽  
Hui Zhang
Keyword(s):  
Decision Making ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Learning Rate ◽  
Stochastic Gradient Descent ◽  
Local Optima ◽  
Q Learning ◽  
Exploration Strategy ◽  
Decision Making Model ◽  
Metropolis Criterion

In this paper, a cognitive electronic jamming decision-making method based on improved Q -learning is proposed to improve the efficiency of radar jamming decision-making. First, the method adopts the simulated annealing (SA) algorithm’s Metropolis criterion to enhance the exploration strategy, balancing the contradictory relationship between exploration and utilization in the algorithm to avoid falling into local optima. At the same time, the idea of stochastic gradient descent with warm restarts (SGDR) is introduced to improve the learning rate of the algorithm, which reduces the oscillation and improves convergence speed at the later stage of the algorithm iteration. Then, a cognitive electronic jamming decision-making model is constructed, and the improved Q -learning algorithm’s specific steps are given. The simulation experiment takes a multifunctional radar as an example to analyze the influence of exploration strategy and learning rate on decision-making performance. The results reveal that compared with the traditional Q -learning algorithm, the improved Q -learning algorithm proposed in this paper can fully explore and efficiently utilize and converge the results to a better solution at a faster speed. The number of iterations can be reduced to more than 50%, which proves the feasibility and effectiveness of the method applied to cognitive electronic jamming decision-making.

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