Reinforcement learning-based collision avoidance: impact of reward function and knowledge transfer

2020 ◽  
Vol 34 (2) ◽  
pp. 207-222
Author(s):  
Xiongqing Liu ◽  
Yan Jin
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Control Strategies ◽  
Design Parameters ◽  
Complex Tasks ◽  
Reward Function ◽  
Multi Stage ◽  
Ship Steering ◽  
Machine Learning Approach ◽  
Reward Functions

AbstractCollision avoidance for robots and vehicles in unpredictable environments is a challenging task. Various control strategies have been developed for the agent (i.e., robots or vehicles) to sense the environment, assess the situation, and select the optimal actions to avoid collision and accomplish its mission. In our research on autonomous ships, we take a machine learning approach to collision avoidance. The lack of available ship steering data of human ship masters has made it necessary to acquire collision avoidance knowledge through reinforcement learning (RL). Given that the learned neural network tends to be a black box, it is desirable that a method is available which can be used to design an agent's behavior so that the desired knowledge can be captured. Furthermore, RL with complex tasks can be either time consuming or unfeasible. A multi-stage learning method is needed in which agents can learn from simple tasks and then transfer their learned knowledge to closely related but more complex tasks. In this paper, we explore the ways of designing agent behaviors through tuning reward functions and devise a transfer RL method for multi-stage knowledge acquisition. The computer simulation-based agent training results have shown that it is important to understand the roles of each component in a reward function and the various design parameters in transfer RL. The settings of these parameters are all dependent on the complexity of the tasks and the similarities between them.

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 LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning

2019 ◽  
Author(s):  
Alberto Camacho ◽  
Rodrigo Toro Icarte ◽  
Toryn Q. Klassen ◽  
Richard Valenzano ◽  
Sheila A. McIlraith
Keyword(s):  
Reinforcement Learning ◽  
Normal Form ◽  
State Of The Art ◽  
Formal Languages ◽  
Function Structure ◽  
Q Learning ◽  
Reward Function ◽  
Form Representation ◽  
Reward Shaping ◽  
Reward Functions

In Reinforcement Learning (RL), an agent is guided by the rewards it receives from the reward function. Unfortunately, it may take many interactions with the environment to learn from sparse rewards, and it can be challenging to specify reward functions that reflect complex reward-worthy behavior. We propose using reward machines (RMs), which are automata-based representations that expose reward function structure, as a normal form representation for reward functions. We show how specifications of reward in various formal languages, including LTL and other regular languages, can be automatically translated into RMs, easing the burden of complex reward function specification. We then show how the exposed structure of the reward function can be exploited by tailored q-learning algorithms and automated reward shaping techniques in order to improve the sample efficiency of reinforcement learning methods. Experiments show that these RM-tailored techniques significantly outperform state-of-the-art (deep) RL algorithms, solving problems that otherwise cannot reasonably be solved by existing approaches.

Driver-like decision-making method for vehicle longitudinal autonomous driving based on deep reinforcement learning

2021 ◽  
pp. 095440702110630
Author(s):  
Zhenhai Gao ◽  
Xiangtong Yan ◽  
Fei Gao ◽  
Lei He
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Autonomous Driving ◽  
Decision Strategies ◽  
Reward Function ◽  
Human Driver ◽  
Reward Functions ◽  
A Current ◽  
Better Than

Decision-making is one of the key parts of the research on vehicle longitudinal autonomous driving. Considering the behavior of human drivers when designing autonomous driving decision-making strategies is a current research hotspot. In longitudinal autonomous driving decision-making strategies, traditional rule-based decision-making strategies are difficult to apply to complex scenarios. Current decision-making methods that use reinforcement learning and deep reinforcement learning construct reward functions designed with safety, comfort, and economy. Compared with human drivers, the obtained decision strategies still have big gaps. Focusing on the above problems, this paper uses the driver’s behavior data to design the reward function of the deep reinforcement learning algorithm through BP neural network fitting, and uses the deep reinforcement learning DQN algorithm and the DDPG algorithm to establish two driver-like longitudinal autonomous driving decision-making models. The simulation experiment compares the decision-making effect of the two models with the driver curve. The results shows that the two algorithms can realize driver-like decision-making, and the consistency of the DDPG algorithm and human driver behavior is higher than that of the DQN algorithm, the effect of the DDPG algorithm is better than the DQN algorithm.

Deep reinforcement learning-based rehabilitation robot trajectory planning with optimized reward functions

2021 ◽  
Vol 13 (12) ◽  
pp. 168781402110670
Author(s):  
Xusheng Wang ◽  
Jiexin Xie ◽  
Shijie Guo ◽  
Yue Li ◽  
Pengfei Sun ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Trajectory Planning ◽  
Working Environment ◽  
Rehabilitation Robot ◽  
Exploration Process ◽  
Reward Function ◽  
Robot Trajectory ◽  
Reward Functions ◽  
Low Efficiency ◽  
Robot Trajectory Planning

Deep reinforcement learning (DRL) provides a new solution for rehabilitation robot trajectory planning in the unstructured working environment, which can bring great convenience to patients. Previous researches mainly focused on optimization strategies but ignored the construction of reward functions, which leads to low efficiency. Different from traditional sparse reward function, this paper proposes two dense reward functions. First, azimuth reward function mainly provides a global guidance and reasonable constraints in the exploration. To further improve the efficiency, a process-oriented aspiration reward function is proposed, it is capable of accelerating the exploration process and avoid locally optimal solution. Experiments show that the proposed reward functions are able to accelerate the convergence rate by 38.4% on average with the mainstream DRL methods. The mean of convergence also increases by 9.5%, and the percentage of standard deviation decreases by 21.2%–23.3%. Results show that the proposed reward functions can significantly improve learning efficiency of DRL methods, and then provide practical possibility for automatic trajectory planning of rehabilitation robot.

scholarly journals Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning

2022 ◽  
Vol 73 ◽  
pp. 173-208
Author(s):  
Rodrigo Toro Icarte ◽  
Toryn Q. Klassen ◽  
Richard Valenzano ◽  
Sheila A. McIlraith
Keyword(s):  
Reinforcement Learning ◽  
Finite State Machine ◽  
Expressive Power ◽  
State Machine ◽  
Function Structure ◽  
Efficient Manner ◽  
Reward Function ◽  
Optimal Policies ◽  
Finite State ◽  
Reward Functions

Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these methods must extensively interact with the environment in order to discover rewards and optimal policies. In most RL applications, however, users have to program the reward function and, hence, there is the opportunity to make the reward function visible – to show the reward function’s code to the RL agent so it can exploit the function’s internal structure to learn optimal policies in a more sample efficient manner. In this paper, we show how to accomplish this idea in two steps. First, we propose reward machines, a type of finite state machine that supports the specification of reward functions while exposing reward function structure. We then describe different methodologies to exploit this structure to support learning, including automated reward shaping, task decomposition, and counterfactual reasoning with off-policy learning. Experiments on tabular and continuous domains, across different tasks and RL agents, show the benefits of exploiting reward structure with respect to sample efficiency and the quality of resultant policies. Finally, by virtue of being a form of finite state machine, reward machines have the expressive power of a regular language and as such support loops, sequences and conditionals, as well as the expression of temporally extended properties typical of linear temporal logic and non-Markovian reward specification.

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 CURIOSITY-DRIVEN REINFORCEMENT LEARNING AGENT FOR MAPPING UNKNOWN INDOOR ENVIRONMENTS

2021 ◽  
Vol V-1-2021 ◽  
pp. 129-136
Author(s):  
N. Botteghi ◽  
R. Schulte ◽  
B. Sirmacek ◽  
M. Poel ◽  
C. Brune
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Indoor Environments ◽  
Reward Function ◽  
Learning Framework ◽  
Learning Agent ◽  
The World ◽  
Reward Functions ◽  
Slam Algorithm

Abstract. Autonomously exploring and mapping is one of the open challenges of robotics and artificial intelligence. Especially when the environments are unknown, choosing the optimal navigation directive is not straightforward. In this paper, we propose a reinforcement learning framework for navigating, exploring, and mapping unknown environments. The reinforcement learning agent is in charge of selecting the commands for steering the mobile robot, while a SLAM algorithm estimates the robot pose and maps the environments. The agent, to select optimal actions, is trained to be curious about the world. This concept translates into the introduction of a curiosity-driven reward function that encourages the agent to steer the mobile robot towards unknown and unseen areas of the world and the map. We test our approach in explorations challenges in different indoor environments. The agent trained with the proposed reward function outperforms the agents trained with reward functions commonly used in the literature for solving such tasks.

scholarly journals Comparing Deep Reinforcement Learning Algorithms’ Ability to Safely Navigate Challenging Waters

2021 ◽  
Vol 8 ◽  
Author(s):  
Thomas Nakken Larsen ◽  
Halvor Ødegård Teigen ◽  
Torkel Laache ◽  
Damiano Varagnolo ◽  
Adil Rasheed
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Path Following ◽  
Behavioral Characteristics ◽  
Training Environment ◽  
Reward Function ◽  
Unexpected Consequence ◽  
Policy Optimization ◽  
Dual Objectives ◽  
And Task

Reinforcement Learning (RL) controllers have proved to effectively tackle the dual objectives of path following and collision avoidance. However, finding which RL algorithm setup optimally trades off these two tasks is not necessarily easy. This work proposes a methodology to explore this that leverages analyzing the performance and task-specific behavioral characteristics for a range of RL algorithms applied to path-following and collision-avoidance for underactuated surface vehicles in environments of increasing complexity. Compared to the introduced RL algorithms, the results show that the Proximal Policy Optimization (PPO) algorithm exhibits superior robustness to changes in the environment complexity, the reward function, and when generalized to environments with a considerable domain gap from the training environment. Whereas the proposed reward function significantly improves the competing algorithms’ ability to solve the training environment, an unexpected consequence of the dimensionality reduction in the sensor suite, combined with the domain gap, is identified as the source of their impaired generalization performance.

scholarly journals Efficient PAC Reinforcement Learning in Regular Decision Processes

2021 ◽  
Author(s):  
Alessandro Ronca ◽  
Giuseppe De Giacomo
Keyword(s):  
Reinforcement Learning ◽  
Markov Decision Process ◽  
Polynomial Time ◽  
Optimal Policy ◽  
Decision Process ◽  
Transition Function ◽  
Decision Processes ◽  
Reward Function ◽  
Markov Decision ◽  
Reward Functions

Recently regular decision processes have been proposed as a well-behaved form of non-Markov decision process. Regular decision processes are characterised by a transition function and a reward function that depend on the whole history, though regularly (as in regular languages). In practice both the transition and the reward functions can be seen as finite transducers. We study reinforcement learning in regular decision processes. Our main contribution is to show that a near-optimal policy can be PAC-learned in polynomial time in a set of parameters that describe the underlying decision process. We argue that the identified set of parameters is minimal and it reasonably captures the difficulty of a regular decision process.

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