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

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 290 ◽  
Author(s):  
SeungYoon Choi ◽  
Tuyen Le ◽  
Quang Nguyen ◽  
Md Layek ◽  
SeungGwan Lee ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
State Of The Art ◽  
The Other ◽  
Gradient Algorithm ◽  
Reward Function ◽  
Policy Gradient ◽  
Policy Optimization ◽  
Start Location

In this paper, we propose a controller for a bicycle using the DDPG (Deep Deterministic Policy Gradient) algorithm, which is a state-of-the-art deep reinforcement learning algorithm. We use a reward function and a deep neural network to build the controller. By using the proposed controller, a bicycle can not only be stably balanced but also travel to any specified location. We confirm that the controller with DDPG shows better performance than the other baselines such as Normalized Advantage Function (NAF) and Proximal Policy Optimization (PPO). For the performance evaluation, we implemented the proposed algorithm in various settings such as fixed and random speed, start location, and destination location.

Preceding vehicle following algorithm with human driving characteristics

2021 ◽  
pp. 095440702098154
Author(s):  
Feng Pan ◽  
Hong Bao
Keyword(s):  
Reinforcement Learning ◽  
Weight Vector ◽  
Gradient Algorithm ◽  
Inner Product ◽  
Inverse Reinforcement Learning ◽  
Reward Function ◽  
Human Driver ◽  
Policy Gradient ◽  
Preceding Vehicle ◽  
Action Spaces

This paper proposes a new approach of using reinforcement learning (RL) to train an agent to perform the task of vehicle following with human driving characteristics. We refer to the ideal of inverse reinforcement learning to design the reward function of the RL model. The factors that need to be weighed in vehicle following were vectorized into reward vectors, and the reward function was defined as the inner product of the reward vector and weights. Driving data of human drivers was collected and analyzed to obtain the true reward function. The RL model was trained with the deterministic policy gradient algorithm because the state and action spaces are continuous. We adjusted the weight vector of the reward function so that the value vector of the RL model could continuously approach that of a human driver. After dozens of rounds of training, we selected the policy with the nearest value vector to that of a human driver and tested it in the PanoSim simulation environment. The results showed the desired performance for the task of an agent following the preceding vehicle safely and smoothly.

scholarly journals Deterministic Value-Policy Gradients

2020 ◽  
Vol 34 (04) ◽  
pp. 3316-3323
Author(s):  
Qingpeng Cai ◽  
Ling Pan ◽  
Pingzhong Tang
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Infinite Horizon ◽  
Gradient Algorithm ◽  
Continuous Control ◽  
Model Bias ◽  
Model Free ◽  
Policy Gradient ◽  
Analytical Gradients

Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider the deterministic value gradients to improve the sample efficiency of deep reinforcement learning algorithms. Previous works consider deterministic value gradients with the finite horizon, but it is too myopic compared with infinite horizon. We firstly give a theoretical guarantee of the existence of the value gradients in this infinite setting. Based on this theoretical guarantee, we propose a class of the deterministic value gradient algorithm (DVG) with infinite horizon, and different rollout steps of the analytical gradients by the learned model trade off between the variance of the value gradients and the model bias. Furthermore, to better combine the model-based deterministic value gradient estimators with the model-free deterministic policy gradient estimator, we propose the deterministic value-policy gradient (DVPG) algorithm. We finally conduct extensive experiments comparing DVPG with state-of-the-art methods on several standard continuous control benchmarks. Results demonstrate that DVPG substantially outperforms other baselines.

Quadrotor Motion Control Using Deep Reinforcement Learning

2021 ◽  
Author(s):  
Zifei Jiang ◽  
Alan F. Lynch
Keyword(s):  
Reinforcement Learning ◽  
Neural Nets ◽  
Neural Net ◽  
Reward Function ◽  
Model Free ◽  
Policy Gradient ◽  
Aerial Vehicle ◽  
Stochastic Controller ◽  
Policy Optimization ◽  
Gradient Approach

We present a deep neural net-based controller trained by a model-free reinforcement learning (RL) algorithm to achieve hover stabilization for a quadrotor unmanned aerial vehicle (UAV). With RL, two neural nets are trained. One neural net is used as a stochastic controller which gives the distribution of control inputs. The other maps the UAV state to a scalar which estimates the reward of the controller. A proximal policy optimization (PPO) method, which is an actor-critic policy gradient approach, is used to train the neural nets. Simulation results show that the trained controller achieves a comparable level of performance to a manually-tuned PID controller, despite not depending on any model information. The paper considers different choices of reward function and their influence on controller performance.

scholarly journals Deep Deterministic Policy Gradient Algorithm Based on Convolutional Block Attention for Autonomous Driving

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1061
Author(s):  
Yanliang Jin ◽  
Qianhong Liu ◽  
Liquan Shen ◽  
Leiji Zhu
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Attention Mechanism ◽  
Gradient Algorithm ◽  
Excellent Performance ◽  
Average Speed ◽  
The Road ◽  
Policy Gradient

The research on autonomous driving based on deep reinforcement learning algorithms is a research hotspot. Traditional autonomous driving requires human involvement, and the autonomous driving algorithms based on supervised learning must be trained in advance using human experience. To deal with autonomous driving problems, this paper proposes an improved end-to-end deep deterministic policy gradient (DDPG) algorithm based on the convolutional block attention mechanism, and it is called multi-input attention prioritized deep deterministic policy gradient algorithm (MAPDDPG). Both the actor network and the critic network of the model have the same structure with symmetry. Meanwhile, the attention mechanism is introduced to help the vehicles focus on useful environmental information. The experiments are conducted in the open racing car simulator (TORCS)and the results of five experiment runs on the test tracks are averaged to obtain the final result. Compared with the state-of-the-art algorithm, the maximum reward increases from 62,207 to 116,347, and the average speed increases from 135 km/h to 193 km/h, while the number of success episodes to complete a circle increases from 96 to 147. Also, the variance of the distance from the vehicle to the center of the road is compared, and the result indicates that the variance of the DDPG is 0.6 m while that of the MAPDDPG is only 0.2 m. The above results indicate that the proposed MAPDDPG achieves excellent performance.

scholarly journals Reward-Free Reinforcement Learning Algorithm Using Prediction Network

2020 ◽  
Author(s):  
Zhen Yu ◽  
Yimin Feng ◽  
Lijun Liu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Value Functions ◽  
Learning Method ◽  
Reward Function ◽  
Network Training ◽  
Learning Tasks ◽  
Reward Value ◽  
Policy Gradient ◽  
Reward Functions

In general reinforcement learning tasks, the formulation of reward functions is a very important step in reinforcement learning. The reward function is not easy to formulate in a large number of systems. The network training effect is sensitive to the reward function, and different reward value functions will get different results. For a class of systems that meet specific conditions, the traditional reinforcement learning method is improved. A state quantity function is designed to replace the reward function, which is more efficient than the traditional reward function. At the same time, the predictive network link is designed so that the network can learn the value of the general state by using the special state. The overall structure of the network will be improved based on the Deep Deterministic Policy Gradient (DDPG) algorithm. Finally, the algorithm was successfully applied in the environment of FrozenLake, and achieved good performance. The experiment proves the effectiveness of the algorithm and realizes rewardless reinforcement learning in a class of systems.

scholarly journals UAV Autonomous Aerial Combat Maneuver Strategy Generation with Observation Error Based on State-Adversarial Deep Deterministic Policy Gradient and Inverse Reinforcement Learning

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1121 ◽  
Author(s):  
Weiren Kong ◽  
Deyun Zhou ◽  
Zhen Yang ◽  
Yiyang Zhao ◽  
Kai Zhang
Keyword(s):  
Reinforcement Learning ◽  
High Performance ◽  
Learning Algorithm ◽  
Gradient Algorithm ◽  
Observation Error ◽  
Inverse Reinforcement Learning ◽  
Generation Algorithm ◽  
Air Combat ◽  
Policy Gradient ◽  
Aerial Combat

With the development of unmanned aerial vehicle (UAV) and artificial intelligence (AI) technology, Intelligent UAV will be widely used in future autonomous aerial combat. Previous researches on autonomous aerial combat within visual range (WVR) have limitations due to simplifying assumptions, limited robustness, and ignoring sensor errors. In this paper, in order to consider the error of the aircraft sensors, we model the aerial combat WVR as a state-adversarial Markov decision process (SA-MDP), which introduce the small adversarial perturbations on state observations and these perturbations do not alter the environment directly, but can mislead the agent into making suboptimal decisions. Meanwhile, we propose a novel autonomous aerial combat maneuver strategy generation algorithm with high-performance and high-robustness based on state-adversarial deep deterministic policy gradient algorithm (SA-DDPG), which add a robustness regularizers related to an upper bound on performance loss at the actor-network. At the same time, a reward shaping method based on maximum entropy (MaxEnt) inverse reinforcement learning algorithm (IRL) is proposed to improve the aerial combat strategy generation algorithm’s efficiency. Finally, the efficiency of the aerial combat strategy generation algorithm and the performance and robustness of the resulting aerial combat strategy is verified by simulation experiments. Our main contributions are three-fold. First, to introduce the observation errors of UAV, we are modeling air combat as SA-MDP. Second, to make the strategy network of air combat maneuver more robust in the presence of observation errors, we introduce regularizers into the policy gradient. Third, to solve the problem that air combat’s reward function is too sparse, we use MaxEnt IRL to design a shaping reward to accelerate the convergence of SA-DDPG.

scholarly journals A Deep Reinforcement Learning Algorithm Based on Tetanic Stimulation and Amnesic Mechanisms for Continuous Control of Multi-DOF Manipulator

Actuators ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 254
Author(s):  
Yangyang Hou ◽  
Huajie Hong ◽  
Dasheng Xu ◽  
Zhe Zeng ◽  
Yaping Chen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Research Area ◽  
Gradient Algorithm ◽  
Data Sets ◽  
Continuous Control ◽  
Tetanic Stimulation ◽  
Policy Gradient ◽  
Active Research

Deep Reinforcement Learning (DRL) has been an active research area in view of its capability in solving large-scale control problems. Until presently, many algorithms have been developed, such as Deep Deterministic Policy Gradient (DDPG), Twin-Delayed Deep Deterministic Policy Gradient (TD3), and so on. However, the converging achievement of DRL often requires extensive collected data sets and training episodes, which is data inefficient and computing resource consuming. Motivated by the above problem, in this paper, we propose a Twin-Delayed Deep Deterministic Policy Gradient algorithm with a Rebirth Mechanism, Tetanic Stimulation and Amnesic Mechanisms (ATRTD3), for continuous control of a multi-DOF manipulator. In the training process of the proposed algorithm, the weighting parameters of the neural network are learned using Tetanic stimulation and Amnesia mechanism. The main contribution of this paper is that we show a biomimetic view to speed up the converging process by biochemical reactions generated by neurons in the biological brain during memory and forgetting. The effectiveness of the proposed algorithm is validated by a simulation example including the comparisons with previously developed DRL algorithms. The results indicate that our approach shows performance improvement in terms of convergence speed and precision.

scholarly journals Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning

2019 ◽  
Vol 9 (20) ◽  
pp. 4198
Author(s):  
Wenzhou Chen ◽  
Shizheng Zhou ◽  
Zaisheng Pan ◽  
Huixian Zheng ◽  
Yong Liu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Gradient Algorithm ◽  
Lidar Data ◽  
Robot System ◽  
Navigation Task ◽  
System A ◽  
Group Navigation ◽  
Policy Gradient ◽  
Multi Robot

Compared with the single robot system, a multi-robot system has higher efficiency and fault tolerance. The multi-robot system has great potential in some application scenarios, such as the robot search, rescue and escort tasks, and so on. Deep reinforcement learning provides a potential framework for multi-robot formation and collaborative navigation. This paper mainly studies the collaborative formation and navigation of multi-robots by using the deep reinforcement learning algorithm. The proposed method improves the classical Deep Deterministic Policy Gradient (DDPG) to address the single robot mapless navigation task. We also extend the single-robot Deep Deterministic Policy Gradient algorithm to the multi-robot system, and obtain the Parallel Deep Deterministic Policy Gradient (PDDPG). By utilizing the 2D lidar sensor, the group of robots can accomplish the formation construction task and the collaborative formation navigation task. The experiment results in a Gazebo simulation platform illustrates that our method is capable of guiding mobile robots to construct the formation and keep the formation during group navigation, directly through raw lidar data inputs.

scholarly journals Application of a Deep Deterministic Policy Gradient Algorithm for Energy-Aimed Timetable Rescheduling Problem

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3461 ◽  
Author(s):  
Guang Yang ◽  
Feng Zhang ◽  
Cheng Gong ◽  
Shiwen Zhang
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Gradient Algorithm ◽  
Q Learning ◽  
Train Timetable ◽  
Continuous State ◽  
Policy Gradient ◽  
Random Disturbances ◽  
Metro Network

Reinforcement learning has potential in the area of intelligent transportation due to its generality and real-time feature. The Q-learning algorithm, which is an early proposed algorithm, has its own merits to solve the train timetable rescheduling (TTR) problem. However, it has shortage in two aspects: Dimensional limits of action and a slow convergence rate. In this paper, a deep deterministic policy gradient (DDPG) algorithm is applied to solve the energy-aimed train timetable rescheduling (ETTR) problem. This algorithm belongs to reinforcement learning, which fulfills real-time requirements of the ETTR problem, and has adaptability on random disturbances. Superior to the Q-learning, DDPG has a continuous state space and action space. After enough training, the learning agent based on DDPG takes proper action by adjusting the cruising speed and the dwelling time continuously for each train in a metro network when random disturbances happen. Although training needs an iteration for thousands of episodes, the policy decision during each testing episode takes a very short time. Models for the metro network, based on a real case of the Shanghai Metro Line 1, are established as a training and testing environment. To validate the energy-saving effect and the real-time feature of the proposed algorithm, four experiments are designed and conducted. Compared with the no action strategy, results show that the proposed algorithm has real-time performance, and saves a significant percentage of energy under random disturbances.

scholarly journals Learning 2-Opt Heuristics for Routing Problems via Deep Reinforcement Learning

2021 ◽  
Vol 2 (5) ◽  
Author(s):  
Paulo da Costa ◽  
Jason Rhuggenaath ◽  
Yingqian Zhang ◽  
Alp Akcay ◽  
Uzay Kaymak
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
State Of The Art ◽  
Gradient Algorithm ◽  
Routing Problems ◽  
Routing Problem ◽  
Local Search Heuristic ◽  
Policy Gradient ◽  
Previous State ◽  
The Traveling Salesman Problem

AbstractRecent works using deep learning to solve routing problems such as the traveling salesman problem (TSP) have focused on learning construction heuristics. Such approaches find good quality solutions but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near-optimal one. In this work, we propose to learn a local search heuristic based on 2-opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which can be easily extended to more general k-opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near-optimal solutions faster than previous state-of-the-art deep learning methods for the TSP. We also show we can adapt the proposed method to two extensions of the TSP: the multiple TSP and the Vehicle Routing Problem, achieving results on par with classical heuristics and learned methods.

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