Quintero, D., Martin, A. E., and Gregg, R. D. (2017). IEEE Int. Generally, the actuator in a microprocessor-controlled prosthetic knee can be divided into two categories: semi-active and active mechanisms. Inspired by such a technique, we implement the reward shaping method in Eq. Sports Exerc. The discounted factor is a variable that determines how the Q-function acts toward the reward. Abstract and Figures Potential-based reward shaping has been shown to be a pow- erful method to improve the convergence rate of reinforce- ment learning agents. Arch. Rehabil. Slowest, mid, and fast walking speeds of 2.4, 3.6, and 5.4 km/h, respectively, are used for training. 26, 305–312. It’s not … Comparison between user adaptive, neural network predictive control (NNPC), and Q-learning control. User-adaptive control of a magnetorheological prosthetic knee. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner’s planning horizon as function of its accuracy: a In this simulation, training multispeed under one control policy is proposed. Recent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient. (B) Effect of various learning rates to the overall performance (normalized root mean squared error, NRMSE). The reward function was designed as a function of the performance index that accounts for the trajectory of the subject-specific knee angle. WK contributed to study conception and design, provided critical review, and supervised the overall study. 92, 66–80. We compared our proposed reward function to a conventional single reward function under the same random initialization of a Q-matrix. Eng. As also observed, a higher learning rate does not guarantee better performance, as inspected from α = 0.9, compared to α = [0.05, 0.1, 0.5]. 2015, 410–415. Potential- Finally, Section 4 discusses the algorithm comparison, the limitations, and the future works of this study. Smart Mater. In order to maintain focused learning, the goal of reward shaping is to promote a low reward horizon. In this study, θK and derivative of knee angle, θK., are used as states, while the command voltage, v, is used as the action. Shaping rewards is hard. There are two conditions for the simulation to stop: first is if all the NRMSE of all trained speed falls under the defined PI criterion, and second is if all the trained speed converges into one final value of NRMSE for at least after 10 further iterations. Med. There are many approaches to train the Q-function in this study. Meanwhile at the initialization stage of learning, action selection follows a greedy policy to explore the Q-function for possible solutions. We found that our proposed reward shaping function leads to better performance in terms of normalized root mean squared error and also showed a faster convergence trend compared to a conventional single reward function. Comparison between user-adaptive control (green dashed line), neural network predictive control (NNPC) (red line), and Q-learning control (black line) for different walking speeds: (A) 2.4 km/h, (B) 3.6 km/h, and (C) 5.4 km/h. Gait asymmetry of transfemoral amputees using mechanical and microprocessor-controlled prosthetic knees. Copyright © 2020 Hutabarat, Ekkachai, Hayashibe and Kongprawechnon. Higher learning rate, which if sets closer to 1, indicates that the Q-function is updated quickly per iteration, while the Q-function is never be updated if it is set to 0. Neural network predictive control (NNPC) was employed as a control structure for the swing phase in the prosthetic knee (Ekkachai and Nilkhamhang, 2016). Med. Figure 4. In this study, we investigated a control algorithm for a semi-active prosthetic knee based on reinforcement learning (RL). The proposed controller was designed with the structure of a tabular reinforcement Q-learning algorithm, a subset in machine learning algorithms. Syst. 1 Introduction Reinforcement learning (RL) is a promising approach to learning control policies for robotics tasks [5, 21, 16, 15]. Before we go further into the details of EPIC and its significance to reinforcement learning, the reader has to be familiar with the concepts of metric spaces and potential-based shaping. doi: 10.1016/j.apmr.2006.10.030, Herr, H., and Wilkenfeld, A. Figure 5. However, it requires an off-line training process to find weight and bias of neural network. A male subject with 83 kg of weight and 1.75 m height at the time of the experiment were asked to walk on a treadmill at various speed, where in this study walking speed was set at 2.4, 3.6, and 5.4 km/h (Ekkachai and Nilkhamhang, 2016). reinforcement learning (RL), one approach is to augment native task rewards with shaping rewards which encourage exploration in areas of the policy space that are more promising according to the prior knowledge. doi: 10.1016/j.ins.2012.12.021, Flynn, L., Geeroms, J., Jimenez-Fabian, R., Vanderborght, B., and Lefeber, D. (2015). |, https://doi.org/10.3389/fnbot.2020.565702, https://www.ossur.com/en-us/prosthetics/knees/power-knee, Creative Commons Attribution License (CC BY). This model was simulated in MATLAB (Mathworks Inc., Natick, MA, USA) SimMechanics environment. Recent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient. Inform. We concluded that the two lowest learning rate (α = 0.001 and α = 0.01) simulated with a constrained iteration of 3,000 performed the worst among other learning rates. Biomed. Science Asia 38, 386–393. The advantages of using this control structure are that it can be trained online, and also it is a model-free control algorithm that does not require prior knowledge of the system to be controlled. Chai, J., and Hayashibe, M. (2020). Gait and balance of transfemoral amputees using passive mechanical and microprocessor-controlled prosthetic knees. 7 This is the most common way human feedback has been applied to RL [1–5]. Here, the proposed Q-learning control is discussed. Motivation Approximating Q-functions Shaping Rewards and Initial Q-Functions Conclusion Reinforcement Learning – Some Weaknesses In the previous lectures, we looked at fundamental temporal difference (TD) methods for reinforcement learning. doi: 10.1109/LRA.2020.2968067, Ekkachai, K., and Nilkhamhang, I. We then measured the moving average of NRMSE parameter with a constrained maximum iterations of 3000 and a fixed learning rate of 0.1. KE supported the development of the system and environment model, collecting datasets, and data analysis. Table 1. is set from −7 to 7° per unit of time with predefined 0.05 step size, thus resulting with 281 columns. On the simulated environment, we have a Q-function block with input of multistate of knee angle from double pendulum model and updated by the reward function. 1. Moreover, in some of the walking speeds, this control structure performs better than the NNPC algorithm. Devices Trans. For the next simulation, we picked learning rate α = 0.5 based on this simulation and considering faster exploration of Q-matrix that could potentially lead to finding better local minimum as solution. Although we cannot provide detailed comparison of our proposed method with another RL-based method in Wen et al. A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rewards for progress towards the goal. In particular, tasks involving human interaction depend on complex and user-dependent preferences. A finite state machine-based controller is often found in the powered knee (Wen et al., 2017). If it is set closer to 0 means, it will only consider the instantaneous reward, while if it is set closer to 1, it strives more into the long-term higher rewards (Sutton and Barto, 2018). Meanwhile, this existing study (Wen et al., 2019) used the RL algorithm to tune a total of 12 impedance parameters of the robotic knee; thus, the output variables are 12. The torque generated by each joint, derived from Lagrange equation, are governed by Equations (2) and (3), where MK and MH are the torques at knee and hip, respectively. Second, this study proposed a tabular-discretized Q-function stored in a Q-matrix. Q-learning belongs to the tabular RL group in the machine learning algorithm. The model consists of two FNNs. The data analyzed in this study is subject to the following licenses/restrictions: datasets analyzed in this article are available upon request. The purpose of reward shaping is to ex- plore how to modify the native reward function without mis- leading the agent. Impact Factor 2.574 | CiteScore 4.6More on impact ›, Advanced Planning, Control, and Signal Processing Methods and Applications in Robotic Systems On the first column, the policy has been trained using reinforcement learning. A key shortcoming of RL is that the user must manually encode the task as a Sutton, R. S., and Barto, A. G. (2018). In Equation (6), βt is the specifically designed ratio of reward priority, n is the number of prediction horizon, and c is a constant that depends on n. In this study, n is set to 4; thus, c = 0.033 to be conveniently compared to the NNPC algorithm studied in Ekkachai and Nilkhamhang (2016) that set the prediction horizon to 4. 2.1 Difference Rewards To use reinforcement learning in a multiagent system, it is important to reward an agent based on its contribution to the system. We implement our approach in a tool called SPECTRL, and show that it outperforms several state-of-the-art baselines. I have a master's degree in Robotics and I write about machine learning advancements. Phys. As Q-learning is following an off-policy method, actions were selected based on the maximum value of the Q-function on the current states, maxQ(s1(t), s2(t)). Reward shaping is a useful method to incorporate auxiliary knowledge safely. Model-free reinforcement Q-learning control with a reward shaping function was proposed as the voltage controller of a magnetorheological damper based on the prosthetic knee. Overall training process of multispeed of walking under one control policy simulation. This control structure also shows adaptability to various walking speeds. Reinforcement Learning: An Introduction. In Equation (4), Q and R are the action-value and reward functions, respectively. Third is to test our proposed control strategy to other subjects and possibly to test a transfer learning approach from control policy that was learnt in this study for dataset from other subjects. However, sparse rewards also slow down learning because the agent needs to take many actions before getting any reward. Neurorobot., 26 November 2020 Cambridge, MA: MIT Press. We used 2.4, 3.6, and 5.4 km/h walking speed dataset, simulated separately with same value of randomized Q-matrix initialization. The design and initial experimental validation of an active myoelectric transfemoral prosthesis. On the applicability point of view, our proposed Q-learning control had no prior knowledge of the structure and characteristics of MR-damper. Plan-based reward shaping for reinforcement learning Abstract: Reinforcement learning, while being a highly popular learning technique for agents and multi-agent systems, has so far encountered difficulties when applying it to more complex domains due to scaling-up problems. We also treated the swing phase as one state, while in Wen et al. Articles, Institute for Infocomm Research (A*STAR), Singapore. Generally, sparse reward functions are easier to define (e.g., get +1 if you win the game, else 0). Learning is rewarded by better rewards that are secured faster and more efficiently. The table depicts that for the walking speed of 2.4 km/h, Q-learning method performed the best with 0.78 of NRMSE, compared to NNPC (0.81) and user-adaptive control (2.70). The MR damper model is shown in Figure 1A. As the controller aims to mimic the biological knee trajectory in the swing phase, the reward will be given according to whether the prosthetic knee can follow the biological knee trajectory. 3 The contribution of this work can be summarised as follows: For further understanding, read the original work: Link to the paper. (C) Proposed reward shaping function as a function of Et. It allows us to analyze the difference from the previous work result keeping the same experimental condition. J. Med. Struct. To capture the respective joints coordinate, reflective markers were placed at hip, knee, and ankle joints. 19:035012. doi: 10.1088/0964-1726/19/3/035012, Sawers, A. Ask Question Asked 2 years ago. IEEE Robot. Wen, Y., Si, J., Brandt, A., Gao, X., and Huang, H. (2019). Further, the reward priority given at the specified prediction horizon is an exponential function, as depicted in Figure 2B. In this section, a simulation of swing phase control using the proposed controller is discussed along with a comparison study. MR damper is defined as the system, that is, the main actuator to be controlled. Each will be reviewed in depth in the following sections. Further, Q-learning algorithm designed for this study is discussed in detail in section 2.2. YH contributed to algorithm design and development, data analysis and interpretation, and writing the first draft. In this section, we introduce the system, the environment model, and the RL algorithm we designed in this study. Reinforcement learning (RL) has enjoyed much recent suc- cess in domains ranging from game-playing to real robotics tasks.

Date Rolls Bon Appétit, Bathroom Storage Shelves, War Of The Spark Vraska Deck, Cypress Tree Tunnel Wikipedia, Roblox Piano Sheets Demon Slayer, Erpnext Vs Odoo Community, Data Trends 2020,