scholarly journals Combining Local and Global Direct Derivative-Free Optimization for Reinforcement Learning

2012 ◽  
Vol 12 (3) ◽  
pp. 53-65 ◽  
Author(s):  
Matteo Leonetti ◽  
Petar Kormushev ◽  
Simone Sagratella
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Target Area ◽  
Policy Space ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Actual Environment ◽  
On Line ◽  
On Line Learning

Abstract We consider the problem of optimization in policy space for reinforcement learning. While a plethora of methods have been applied to this problem, only a narrow category of them proved feasible in robotics. We consider the peculiar characteristics of reinforcement learning in robotics, and devise a combination of two algorithms from the literature of derivative-free optimization. The proposed combination is well suited for robotics, as it involves both off-line learning in simulation and on-line learning in the real environment. We demonstrate our approach on a real-world task, where an Autonomous Underwater Vehicle has to survey a target area under potentially unknown environment conditions. We start from a given controller, which can perform the task under foreseeable conditions, and make it adaptive to the actual environment.

Analyzing Strength-Based Classifier System from Reinforcement Learning Perspective

2009 ◽  
Vol 13 (6) ◽  
pp. 631-639
Author(s):  
Atsushi Wada ◽  
◽  
Keiki Takadama ◽  
◽  
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Systems ◽  
Classifier Systems ◽  
Q Learning ◽  
State Action ◽  
Classifier System ◽  
Learning Classifier ◽  
Value Estimation ◽  
On Line ◽  
On Line Learning

Learning Classifier Systems (LCSs) are rule-based adaptive systems that have both Reinforcement Learning (RL) and rule-discovery mechanisms for effective and practical on-line learning. With the aim of establishing a common theoretical basis between LCSs and RL algorithms to share each field's findings, a detailed analysis was performed to compare the learning processes of these two approaches. Based on our previous work on deriving an equivalence between the Zeroth-level Classifier System (ZCS) and Q-learning with Function Approximation (FA), this paper extends the analysis to the influence of actually applying the conditions for this equivalence. Comparative experiments have revealed interesting implications: (1) ZCS's original parameter, the deduction rate, plays a role in stabilizing the action selection, but (2) from the Reinforcement Learning perspective, such a process inhibits the ability to accurately estimate values for the entire state-action space, thus limiting the performance of ZCS in problems requiring accurate value estimation.

Autonomous underwater vehicle path planning based on actor-multi-critic reinforcement learning

2020 ◽  
pp. 095965182093708
Author(s):  
Zhuo Wang ◽  
Shiwei Zhang ◽  
Xiaoning Feng ◽  
Yancheng Sui
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Value Function ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Learning Efficiency ◽  
Environmental Adaptability ◽  
Vehicle Path ◽  
The Value Function

The environmental adaptability of autonomous underwater vehicles is always a problem for its path planning. Although reinforcement learning can improve the environmental adaptability, the slow convergence of reinforcement learning is caused by multi-behavior coupling, so it is difficult for autonomous underwater vehicle to avoid moving obstacles. This article proposes a multi-behavior critic reinforcement learning algorithm applied to autonomous underwater vehicle path planning to overcome problems associated with oscillating amplitudes and low learning efficiency in the early stages of training which are common in traditional actor–critic algorithms. Behavior critic reinforcement learning assesses the actions of the actor from perspectives such as energy saving and security, combining these aspects into a whole evaluation of the actor. In this article, the policy gradient method is selected as the actor part, and the value function method is selected as the critic part. The strategy gradient and the value function methods for actor and critic, respectively, are approximated by a backpropagation neural network, the parameters of which are updated using the gradient descent method. The simulation results show that the method has the ability of optimizing learning in the environment and can improve learning efficiency, which meets the needs of real time and adaptability for autonomous underwater vehicle dynamic obstacle avoidance.

scholarly journals Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 24258-24268 ◽  
Author(s):  
Qilei Zhang ◽  
Jinying Lin ◽  
Qixin Sha ◽  
Bo He ◽  
Guangliang Li
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Underwater Vehicle ◽  
Path Following ◽  
Underwater Vehicle
Sign in / Sign up

Export Citation Format

Share Document