Universal Artificial Pheromone Framework with Deep Reinforcement Learning for Robotic Systems

2021 ◽  
Author(s):  
Seongin Na ◽  
Hanlin Niu ◽  
Barry Lennox ◽  
Farshad Arvin
Keyword(s):  
Reinforcement Learning ◽  
Robotic Systems ◽  
Artificial Pheromone

scholarly journals Online Optimal Control of Robotic Systems with Single Critic NN-Based Reinforcement Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiaoyi Long ◽  
Zheng He ◽  
Zhongyuan Wang
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Tracking Error ◽  
Adaptive Dynamic Programming ◽  
Robotic Systems ◽  
Control Synthesis ◽  
Optimal Tracking ◽  
Optimal Tracking Control

This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)-based reinforcement learning (RL) method. To this end, we rewrite the robotic system model as a state-space form, which will facilitate the realization of optimal tracking control synthesis. To maintain the tracking response, a steady-state control is designed, and then an adaptive optimal tracking control is used to ensure that the tracking error can achieve convergence in an optimal sense. To solve the obtained optimal control via the framework of adaptive dynamic programming (ADP), the command trajectory to be tracked and the modified tracking Hamilton-Jacobi-Bellman (HJB) are all formulated. An online RL algorithm is the developed to address the HJB equation using a critic NN with online learning algorithm. Simulation results are given to verify the effectiveness of the proposed method.

scholarly journals Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges

Robotics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Sarthak Bhagat ◽  
Hritwick Banerjee ◽  
Zion Ho Tse ◽  
Hongliang Ren
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Degrees Of Freedom ◽  
Review Article ◽  
Imitation Learning ◽  
Robotic Systems ◽  
Soft Robotics ◽  
Flexible Robots ◽  
Increasing Trend ◽  
Embodied Intelligence

The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics.

Combat Robot Strategy Adaptation Using Multiple Learning Agents

2012 ◽  
Author(s):  
Thomas Recchia ◽  
Jae Chung ◽  
Kishore Pochiraju
Keyword(s):  
Reinforcement Learning ◽  
Robotic Systems ◽  
Multi Agent System ◽  
Learning Agents ◽  
Loosely Coupled ◽  
Reward Function ◽  
Strategy Adaptation ◽  
Agent Learning ◽  
Multi Agent ◽  
Reward Functions

As robotic systems become more prevalent, it is highly desirable for them to be able to operate in highly dynamic environments. A common approach is to use reinforcement learning to allow an agent controlling the robot to learn and adapt its behavior based on a reward function. This paper presents a novel multi-agent system that cooperates to control a single robot battle tank in a melee battle scenario, with no prior knowledge of its opponents’ strategies. The agents learn through reinforcement learning, and are loosely coupled by their reward functions. Each agent controls a different aspect of the robot’s behavior. In addition, the problem of delayed reward is addressed through a time-averaged reward applied to several sequential actions at once. This system was evaluated in a simulated melee combat scenario and was shown to learn to improve its performance over time. This was accomplished by each agent learning to pick specific battle strategies for each different opponent it faced.

scholarly journals BOLeRo: Behavior optimization and learning for robots

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091374
Author(s):  
Alexander Fabisch ◽  
Malte Langosz ◽  
Frank Kirchner
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithms ◽  
Open Source ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Robotic Systems ◽  
Policy Search ◽  
Behavior Learning ◽  
And Behavior

Reinforcement learning and behavior optimization are becoming more and more popular in the field of robotics because algorithms are mature enough to tackle real problems in this domain. Robust implementations of state-of-the-art algorithms are often not publicly available though, and experiments are hardly reproducible because open-source implementations are often not available or are still in a stage of research code. Consequently, often it is infeasible to deploy these algorithms on robotic systems. BOLeRo closes this gap for policy search and evolutionary algorithms by delivering open-source implementations of behavior learning algorithms for robots. It is easy to integrate in robotic middlewares and it can be used to compare methods and develop prototypes in simulation.

scholarly journals On the Impact of Gravity Compensation on Reinforcement Learning in Goal-Reaching Tasks for Robotic Manipulators

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 46
Author(s):  
Jonathan Fugal ◽  
Jihye Bae ◽  
Hasan A. Poonawala
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Systems ◽  
Learning Technologies ◽  
Robotic Systems ◽  
Gravity Compensation ◽  
Reaching Tasks ◽  
Combined Control ◽  
Classical Control ◽  
The Impact ◽  
Target Locations

Advances in machine learning technologies in recent years have facilitated developments in autonomous robotic systems. Designing these autonomous systems typically requires manually specified models of the robotic system and world when using classical control-based strategies, or time consuming and computationally expensive data-driven training when using learning-based strategies. Combination of classical control and learning-based strategies may mitigate both requirements. However, the performance of the combined control system is not obvious given that there are two separate controllers. This paper focuses on one such combination, which uses gravity-compensation together with reinforcement learning (RL). We present a study of the effects of gravity compensation on the performance of two reinforcement learning algorithms when solving reaching tasks using a simulated seven-degree-of-freedom robotic arm. The results of our study demonstrate that gravity compensation coupled with RL can reduce the training required in reaching tasks involving elevated target locations, but not all target locations.

Reinforcement Learning endowed with safe veto policies to learn the control of Linked-Multicomponent Robotic Systems

2015 ◽  
Vol 317 ◽  
pp. 25-47 ◽  
Author(s):  
Borja Fernandez-Gauna ◽  
Manuel Graña ◽  
Jose Manuel Lopez-Guede ◽  
Ismael Etxeberria-Agiriano ◽  
Igor Ansoategui
Keyword(s):  
Reinforcement Learning ◽  
Robotic Systems
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