scholarly journals Learning an Efficient Gait Cycle of a Biped Robot Based on Reinforcement Learning and Artificial Neural Networks

2019 ◽  
Vol 9 (3) ◽  
pp. 502 ◽  
Author(s):  
Cristyan Gil ◽  
Hiram Calvo ◽  
Humberto Sossa
Keyword(s):  
Reinforcement Learning ◽  
Gait Cycle ◽  
Biped Robot ◽  
Q Learning ◽  
Nao Robot ◽  
Simulated Environment ◽  
Proposed Model ◽  
Normal Speed ◽  
Multi Level ◽  
Efficient Gait

Programming robots for performing different activities requires calculating sequences of values of their joints by taking into account many factors, such as stability and efficiency, at the same time. Particularly for walking, state of the art techniques to approximate these sequences are based on reinforcement learning (RL). In this work we propose a multi-level system, where the same RL method is used first to learn the configuration of robot joints (poses) that allow it to stand with stability, and then in the second level, we find the sequence of poses that let it reach the furthest distance in the shortest time, while avoiding falling down and keeping a straight path. In order to evaluate this, we focus on measuring the time it takes for the robot to travel a certain distance. To our knowledge, this is the first work focusing both on speed and precision of the trajectory at the same time. We implement our model in a simulated environment using q-learning. We compare with the built-in walking modes of an NAO robot by improving normal-speed and enhancing robustness in fast-speed. The proposed model can be extended to other tasks and is independent of a particular robot model.

scholarly journals Deep Reinforcement Learning by Balancing Offline Monte Carlo and Online Temporal Difference Use Based on Environment Experiences

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1685 ◽  
Author(s):  
Chayoung Kim
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Real Time ◽  
Temporal Difference ◽  
Q Learning ◽  
State Action ◽  
Proposed Model ◽  
Reward Functions ◽  
And Performance ◽  
The Internet Of Things

Owing to the complexity involved in training an agent in a real-time environment, e.g., using the Internet of Things (IoT), reinforcement learning (RL) using a deep neural network, i.e., deep reinforcement learning (DRL) has been widely adopted on an online basis without prior knowledge and complicated reward functions. DRL can handle a symmetrical balance between bias and variance—this indicates that the RL agents are competently trained in real-world applications. The approach of the proposed model considers the combinations of basic RL algorithms with online and offline use based on the empirical balances of bias–variance. Therefore, we exploited the balance between the offline Monte Carlo (MC) technique and online temporal difference (TD) with on-policy (state-action–reward-state-action, Sarsa) and an off-policy (Q-learning) in terms of a DRL. The proposed balance of MC (offline) and TD (online) use, which is simple and applicable without a well-designed reward, is suitable for real-time online learning. We demonstrated that, for a simple control task, the balance between online and offline use without an on- and off-policy shows satisfactory results. However, in complex tasks, the results clearly indicate the effectiveness of the combined method in improving the convergence speed and performance in a deep Q-network.

scholarly journals Representation Learning for Grounded Spatial Reasoning

2018 ◽  
Vol 6 ◽  
pp. 49-61 ◽  
Author(s):  
Michael Janner ◽  
Karthik Narasimhan ◽  
Regina Barzilay
Keyword(s):  
Reinforcement Learning ◽  
Spatial Reasoning ◽  
State Of The Art ◽  
Representation Learning ◽  
Localization Error ◽  
Value Iteration ◽  
Joint Inference ◽  
Simulated Environment ◽  
Proposed Model ◽  
The World

The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.

Intelligent scheduling using a neural network model in conjunction with reinforcement learning

2005 ◽  
Vol 219 (2) ◽  
pp. 229-235
Author(s):  
C J Fourie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Learning From Experience ◽  
Scheduling System ◽  
Simulated Environment ◽  
The Neural Network ◽  
Learning Techniques ◽  
Proposed Model ◽  
Intelligent Scheduling

This paper describes the use of an artificial neural network in conjunction with reinforcement learning techniques to develop an intelligent scheduling system that is capable of learning from experience. In a simulated environment the model controls a mobile robot that transports material to machines. States of ‘happiness’ are defined for each machine, which are the inputs to the neural network. The output of the neural network is the decision on which machine to service next. After every decision, a critic evaluates the decision and a teacher ‘rewards’ the network to encourage good decisions and discourage bad decisions. From the results obtained, it is concluded that the proposed model is capable of learning from past experience and thereby improving the intelligence of the system.

scholarly journals Personalized project recommendations: using reinforcement learning

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Faxin Qi ◽  
Xiangrong Tong ◽  
Lei Yu ◽  
Yingjie Wang
Keyword(s):  
Reinforcement Learning ◽  
User Behavior ◽  
Collaborative Work ◽  
Recursive Least Squares ◽  
The Internet ◽  
Dynamic Impact ◽  
Rls Algorithm ◽  
Trust Value ◽  
Q Learning ◽  
Actual Evaluation

AbstractWith the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.

scholarly journals Integrating Production Planning with Truck-Dispatching Decisions through Reinforcement Learning While Managing Uncertainty

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 587
Author(s):  
Joao Pedro de Carvalho ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Reinforcement Learning ◽  
Discrete Event ◽  
Mining Operations ◽  
Fixed Sequence ◽  
Q Learning ◽  
Reward Function ◽  
Copper Gold ◽  
Mining Complex ◽  
Learning Reinforcement ◽  
Operational Plan

This paper presents a new truck dispatching policy approach that is adaptive given different mining complex configurations in order to deliver supply material extracted by the shovels to the processors. The method aims to improve adherence to the operational plan and fleet utilization in a mining complex context. Several sources of operational uncertainty arising from the loading, hauling and dumping activities can influence the dispatching strategy. Given a fixed sequence of extraction of the mining blocks provided by the short-term plan, a discrete event simulator model emulates the interaction arising from these mining operations. The continuous repetition of this simulator and a reward function, associating a score value to each dispatching decision, generate sample experiences to train a deep Q-learning reinforcement learning model. The model learns from past dispatching experience, such that when a new task is required, a well-informed decision can be quickly taken. The approach is tested at a copper–gold mining complex, characterized by uncertainties in equipment performance and geological attributes, and the results show improvements in terms of production targets, metal production, and fleet management.

scholarly journals Improved Modeling of a Multi-Level Inverter for TACS to Reduce Computational Time and Improve Accuracy

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 849
Author(s):  
Sung-An Kim
Keyword(s):  
Mechanical Model ◽  
Simulation Software ◽  
Computational Time ◽  
Electrical Model ◽  
Conventional Model ◽  
Switching Operation ◽  
Air Compressor ◽  
Power Semiconductors ◽  
Proposed Model ◽  
Multi Level

A modeling of a turbo air compressor system (TACS), with a multi-level inverter for driving variable speed, combining an electrical model of an electric motor drive system (EMDS) and a mechanical model of a turbo air compressor, is essential to accurately analyze dynamics characteristics. Compared to the mechanical model, the electrical model has a short sampling time due to the high frequency switching operation of the numerous power semiconductors inside the multi-level inverter. This causes the problem of increased computational time for dynamic characteristics analysis of TACS. To solve this problem, the conventional model of the multi-level inverter has been proposed to simplify the switching operation of the power semiconductors, however it has low accuracy because it does not consider pulse width modulation (PWM) operation. Therefore, this paper proposes an improved modeling of the multi-level inverter for TACS to reduce computational time and improve the accuracy of electrical and mechanical responses. In order to verify the reduced computational time of the proposed model, the conventional model using the simplified model is compared and analyzed using an electronic circuit simulation software PSIM. Then, the improved accuracy of the proposed model is verified by comparison with the experimental results.

scholarly journals Aircraft Maintenance Check Scheduling Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 113
Author(s):  
Pedro Andrade ◽  
Catarina Silva ◽  
Bernardete Ribeiro ◽  
Bruno F. Santos
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Q Learning ◽  
Scheduling Policy ◽  
Real Scenario ◽  
Maintenance Plan ◽  
Small Disturbances

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

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.

scholarly journals Comparing quantum hybrid reinforcement learning to classical methods

2021 ◽  
Author(s):  
Maximilian Moll ◽  
Leonhard Kunczik
Keyword(s):  
Reinforcement Learning ◽  
Quantum Circuits ◽  
Memory Usage ◽  
Computational Power ◽  
Classical Domain ◽  
Q Learning ◽  
Optimal Behavior ◽  
Computational Speed ◽  
Complex Decision ◽  
Hybrid Reinforcement

AbstractIn recent history, reinforcement learning (RL) proved its capability by solving complex decision problems by mastering several games. Increased computational power and the advances in approximation with neural networks (NN) paved the path to RL’s successful applications. Even though RL can tackle more complex problems nowadays, it still relies on computational power and runtime. Quantum computing promises to solve these issues by its capability to encode information and the potential quadratic speedup in runtime. We compare tabular Q-learning and Q-learning using either a quantum or a classical approximation architecture on the frozen lake problem. Furthermore, the three algorithms are analyzed in terms of iterations until convergence to the optimal behavior, memory usage, and runtime. Within the paper, NNs are utilized for approximation in the classical domain, while in the quantum domain variational quantum circuits, as a quantum hybrid approximation method, have been used. Our simulations show that a quantum approximator is beneficial in terms of memory usage and provides a better sample complexity than NNs; however, it still lacks the computational speed to be competitive.

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