scholarly journals A Study on an Enhanced Autonomous Driving Simulation Model Based on Reinforcement Learning Using a Collision Prevention Model

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2271
Author(s):  
Jong-Hoon Kim ◽  
Jun-Ho Huh ◽  
Se-Hoon Jung ◽  
Chun-Bo Sim
Keyword(s):  
Reinforcement Learning ◽  
Performance Test ◽  
Learning Algorithm ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Test Results ◽  
Learning Agents ◽  
Collision Prevention ◽  
Prevention Model ◽  
Definition Of

This paper set out to revise and improve existing autonomous driving models using reinforcement learning, thus proposing a reinforced autonomous driving prediction model. The paper conducted training for a reinforcement learning model using DQN, a reinforcement learning algorithm. The main aim of this paper was to reduce the time spent on training and improve self-driving performance. Rewards for reinforcement learning agents were developed to mimic human driving behavior as much as possible. High rewards were given for greater distance travelled within lanes and higher speed. Negative rewards were given when a vehicle crossed into other lanes or had a collision. Performance evaluation was carried out in urban environments without pedestrians. The performance test results show that the model with the collision prevention model exhibited faster performance improvement within the same time compared to when the model was not applied. However, vulnerabilities to factors such as pedestrians and vehicles approaching from the side were not addressed, and the lack of stability in the definition of compensation functions and limitations with respect to the excessive use of memory were shown.

scholarly journals KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge

2020 ◽  
Author(s):  
Peng Zhang ◽  
Jianye Hao ◽  
Weixun Wang ◽  
Hongyao Tang ◽  
Yi Ma ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Prior Knowledge ◽  
Learning Process ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Policy Network ◽  
Human Knowledge ◽  
Learning Agents ◽  
The Common ◽  
Low Performance

Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment. This is quite different from the learning process of human. When faced with a new task, human naturally have the common sense and use the prior knowledge to derive an initial policy and guide the learning process afterwards. Although the prior knowledge may be not fully applicable to the new task, the learning process is significantly sped up since the initial policy ensures a quick-start of learning and intermediate guidance allows to avoid unnecessary exploration. Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning. Our framework consists of a fuzzy rule controller to represent human knowledge and a refine module to finetune suboptimal prior knowledge. The proposed framework is end-to-end and can be combined with existing policy-based reinforcement learning algorithm. We conduct experiments on several control tasks. The empirical results show that our approach, which combines suboptimal human knowledge and RL, achieves significant improvement on learning efficiency of flat RL algorithms, even with very low-performance human prior knowledge.

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%.

scholarly journals Learning to Teach Reinforcement Learning Agents

2017 ◽  
Vol 1 (1) ◽  
pp. 21-42 ◽  
Author(s):  
Anestis Fachantidis ◽  
Matthew Taylor ◽  
Ioannis Vlahavas
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Relevant Literature ◽  
Learning Approaches ◽  
Training Time ◽  
Factors Affecting ◽  
Learning Agents ◽  
Best Teachers ◽  
Previous Learning ◽  
Advice Quality

In this article, we study the transfer learning model of action advice under a budget. We focus on reinforcement learning teachers providing action advice to heterogeneous students playing the game of Pac-Man under a limited advice budget. First, we examine several critical factors affecting advice quality in this setting, such as the average performance of the teacher, its variance and the importance of reward discounting in advising. The experiments show that the best performers are not always the best teachers and reveal the non-trivial importance of the coefficient of variation (CV) as a statistic for choosing policies that generate advice. The CV statistic relates variance to the corresponding mean. Second, the article studies policy learning for distributing advice under a budget. Whereas most methods in the relevant literature rely on heuristics for advice distribution, we formulate the problem as a learning one and propose a novel reinforcement learning algorithm capable of learning when to advise or not. The proposed algorithm is able to advise even when it does not have knowledge of the student’s intended action and needs significantly less training time compared to previous learning approaches. Finally, in this article, we argue that learning to advise under a budget is an instance of a more generic learning problem: Constrained Exploitation Reinforcement Learning.

scholarly journals BUILDING AN ARTIFICIAL STOCK MARKET POPULATED BY REINFORCEMENT‐LEARNING AGENTS

2009 ◽  
Vol 10 (4) ◽  
pp. 329-341 ◽  
Author(s):  
Aleksandras Vytautas Rutkauskas ◽  
Tomas Ramanauskas
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Learning Algorithm ◽  
Self Regulation ◽  
Market Model ◽  
Emergent Properties ◽  
Q Learning ◽  
Evolutionary Selection ◽  
Learning Agents ◽  
Artificial Stock Market

In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement-learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision-making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup. Along with that a parallel version of the model is presented, which is mainly based on research of current changes in the market, as well as on search of newly emerged consistent patterns, and which has been repeatedly used for optimal decisions’ search experiments in various capital markets.

scholarly journals Research on Tensor-Based Cooperative and Competitive in Multi-Agent Reinforcement Learning

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Tsega Weldu Araya ◽  
Md Rashed Ibn Nawab ◽  
A. P. Yuan Ling
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Data Representation ◽  
Training Data ◽  
Two Dimensional ◽  
Multiple Agents ◽  
Learning Agents ◽  
Dimensional Array ◽  
Multi Agent ◽  
Agent Cooperation

As technology overgrows, the assortment of information and the density of work becomes demanding to manage. To resolve the density of employment and human labor, machine-learning (ML) technology developed. Reinforcement learning (RL) is the recent advancement of ML studies. Multi-agent reinforcement learning (MARL) is useful to train multiple agents in the surrounding environment. The previous research studies focused on two-agent cooperation. Their data representation was held in a two-dimensional array, which is called a matrix. The limitation of this two-dimensional array appears as the training data of agents increases. The growth in the training data of agents creates storage drawbacks and data redundancy. Our first aim in this research is to improve an algorithm that can represent MARL training in tensor. In MARL, multiple agents are work together to achieve joint work. To share the training records and data of numerous agents, we need to collect the previous cumulative experience of agents in tensor. Secondly, we will discover the agent's cooperation and competition, with local and global goals of agents in MARL. Local goals are the cooperation of agents in a group or team where we use the training model as a student and teacher agent. The global goal is the competition between two contrary teams to acquire the reward. All learning agents have their Q table for storing the individual agent's training data in an environment. The growth in the number of learning agents, their training experience in Q tables, and the requirement for representing multiple data become the most challenging issue. We introduce tensor to store various data to resolve the challenges for data representation in multiple agent associations. Tensor is expressed as the three-dimensional array, although it is an N-way array, which is useful for representing and accessing numerous data. Finally, we will implement an algorithm for learning three cooperative agents against the opposed team using a tensor-based framework in the Q learning algorithm. We will provide an algorithm that can store the training records and data of multiple agents. Tensor advances to get a small storage size than the matrix for the training records of agents. Although three agent cooperation benefits to having maximum optimal reward.

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 Autonomous Drifting Using Reinforcement Learning

2021 ◽  
Author(s):  
László Orgován ◽  
Tamás Bécsi ◽  
Szilárd Aradi
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Road ◽  
Model Free ◽  
On The Road ◽  
Self Driving Cars ◽  
Planning Problems

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

Sign in / Sign up

Export Citation Format

Share Document