Tag Archives: Deep Reinforcement Learning

Dealing with the exploration with a nice introduction to the problem

October 20, 2023 09:22 ,

Jiayi Lu, Shuai Han, Shuai L�, Meng Kang, Junwei Zhang, Sampling diversity driven exploration with state difference guidance, Expert Systems with Applications, Volume 203, 2022 DOI: 10.1016/j.eswa.2022.117418.

Exploration is one of the key issues of deep reinforcement learning, especially in the environments with sparse or deceptive rewards. Exploration based on intrinsic rewards can handle these environments. However, these methods cannot take both global interaction dynamics and local environment changes into account simultaneously. In this paper, we propose a novel intrinsic reward for off-policy learning, which not only encourages the agent to take actions not fully learned from a global perspective, but also instructs the agent to trigger remarkable changes in the environment from a local perspective. Meanwhile, we propose the double-actors\u2013double-critics framework to combine intrinsic rewards with extrinsic rewards to avoid the inappropriate combination of intrinsic and extrinsic rewards in previous methods. This framework can be applied to off-policy learning algorithms based on the actor\u2013critic method. We provide a comprehensive evaluation of our approach on the MuJoCo benchmark environments. The results demonstrate that our method can perform effective exploration in the environments with dense, deceptive and sparse rewards. Besides, we conduct sufficient ablation and quantitative analyses to intrinsic rewards. Furthermore, we also verify the superiority and rationality of our double-actors\u2013double-critics framework through comparative experiments.

Posted in: Reinforcement learning in AI , Tagged: ,

Increasing exploration when the agent performs worse, decreasing when performing better, in the context of DQN for distributing computation among cloud and edge servers, also dealing with hybridization of RL with Fuzzy

October 20, 2023 09:13 ,

Do Bao Son, Ta Huu Binh, Hiep Khac Vo, Binh Minh Nguyen, Huynh Thi Thanh Binh, Shui Yu, Value-based reinforcement learning approaches for task offloading in Delay Constrained Vehicular Edge Computing, Engineering Applications of Artificial Intelligence, Volume 113, 2022 DOI: 10.1016/j.engappai.2022.104898.

In the age of booming information technology, human-being has witnessed the need for new paradigms with both high computational capability and low latency. A potential solution is Vehicular Edge Computing (VEC). Previous work proposed a Fuzzy Deep Q-Network in Offloading scheme (FDQO) that combines Fuzzy rules and Deep Q-Network (DQN) to improve DQN\u2019s early performance by using Fuzzy Controller (FC). However, we notice that frequent usage of FC can hinder the future growth performance of model. One way to overcome this issue is to remove Fuzzy Controller entirely. We introduced an algorithm called baseline DQN (b-DQN), represented by its two variants Static baseline DQN (Sb-DQN) and Dynamic baseline DQN (Db-DQN), to modify the exploration rate base on the average rewards of closest observations. Our findings confirm that these baseline DQN algorithms surpass traditional DQN models in terms of average Quality of Experience (QoE) in 100 time slots by about 6%, but still suffer from poor early performance (such as in the first 5 time slots). Here, we introduce baseline FDQO (b-FDQO). This algorithm has a strategy to modify the Fuzzy Logic usage instead of removing it entirely while still observing the rewards to modify the exploration rate. It brings a higher average QoE in the first 5 time slots compared to other non-fuzzy-logic algorithms by at least 55.12%, prevent the model from getting too bad result over all time slots, while having the late performance as good as that of b-DQN.

Posted in: Reinforcement learning in AI , Tagged: , ,

Generating contrafactual explanations of Deep RL decisions to identify flawed agents

July 11, 2023 08:44 ,

Matthew L. Olson, Roli Khanna, Lawrence Neal, Fuxin Li, Weng-Keen Wong, Counterfactual state explanations for reinforcement learning agents via generative deep learning, . Artificial Intelligence, Volume 295, 2021 DOI: 10.1016/j.artint.2021.103455.

Counterfactual explanations, which deal with “why not?” scenarios, can provide insightful explanations to an AI agent’s behavior [Miller [38]]. In this work, we focus on generating counterfactual explanations for deep reinforcement learning (RL) agents which operate in visual input environments like Atari. We introduce counterfactual state explanations, a novel example-based approach to counterfactual explanations based on generative deep learning. Specifically, a counterfactual state illustrates what minimal change is needed to an Atari game image such that the agent chooses a different action. We also evaluate the effectiveness of counterfactual states on human participants who are not machine learning experts. Our first user study investigates if humans can discern if the counterfactual state explanations are produced by the actual game or produced by a generative deep learning approach. Our second user study investigates if counterfactual state explanations can help non-expert participants identify a flawed agent; we compare against a baseline approach based on a nearest neighbor explanation which uses images from the actual game. Our results indicate that counterfactual state explanations have sufficient fidelity to the actual game images to enable non-experts to more effectively identify a flawed RL agent compared to the nearest neighbor baseline and to having no explanation at all.

Posted in: Reinforcement learning in AI , Tagged: ,

Summary of the state of the art and current challenges of Deep RL in Robotics

July 3, 2023 08:43 ,

Ibarz J, Tan J, Finn C, Kalakrishnan M, Pastor P, Levine S., How to train your robot with deep reinforcement learning: lessons we have learned, . The International Journal of Robotics Research. 2021;40(4-5):698-721 DOI: 10.1177/0278364920987859.

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

NOTES:

  • Interesting summary of the state of the arts and algorithms used.
  • Defining reward beforehand partly defeats the primary goal of learning by itself.
  • Re-using experiences gathered for learning a task for other tasks, since experiences are mostly task-independent.
  • The problem of leaving the robot unattended while learning, and of mechanism damages and wear-tear. “Learning physically requires human presence for resetting experiments, monitoring hardware status and ensuring safety”. “The majority of robot learning experiments to date were conducted on a single robot closely monitored by a single human operator. This one-to-one relation between robot and operator has been a tedious but effective way to ensure continuous and safe operation. The human can reset the scene, stop the robot in unsafe situations, and simply restart and reset the robot on failures. However, to scale up data collection efforts and increase the throughput of evaluation runs, robots need to run without human supervision. It is impractical to allocate more operators to a set-up with multiple robots, or whenever a single robot is meant to run 24/7, and especially both.” “Repeated falling, self-collisions, jerky actuation, and collisions with obstacles may damage the robot and its surroundings, which will require costly repairs and manual interventions ” “We use the term robot persistence to refer to the capability of the robot to persist in collecting data and training with minimal human intervention.”
  • The Reality Gap can be very important, and so the life-long adaptation. “The reality gap is a major obstacle that prevents the application of learning to robotics”. “we found that the actuator dynamics and the lack of latency modeling are the main causes of the model error” in the reality gap. “Hardware degradation, such as change of battery level, wear and tear, and hardware failure, are the major causes of dynamic changes”
  • Recognizing dangerous situations: section 4.11.3, even learn them.
  • Importance of learning bad situations together with good situations: “to add demonstration data to the data buffer for the off-policy algorithm” -> “tends to be problematic in practice, because commonly used approximate dynamic programming methods (i.e., value function estimation) need to see both good and bad experience to learn which actions are desirable. Therefore, when the demonstrations are much better than the agent’s own experience, the value function will typically learn that the demonstrated states are better, but might fail to learn which actions must be taken to reach those states.” -> can be intertwined together, mixing their results into one (“joint training”) -> better to learn the models in model-based.
  • Simulation is needed to reduce the effort of real learning.”In the last few years, the OpenAI Gym benchmark (Brockman et al., 2016) is the key driving force behind the development of deep RL and its application to robotics”
  • “Generally speaking, among model-free techniques, off-policy methods are about an order of magnitude more data efficient than on-policy methods. Model-based methods could be another order of magnitude more data efficient than their model-free counterparts.”
  • The presence of delays in the learning loop compromises Markovianity and thus RL performance (sect. 4.8). These delays are not covered by simulators. Compensating delay techniques are addressed in sect. 4.3.1. “Latency measures the delay from when the observation is measured at the sensor, to when the action is actually executed at the actuator. This delay is usually on the order of milliseconds to seconds, depending on the hardware and the complexity of the policy. The existence of latency means that the next state of the system does not directly depend on the measured state, but instead on the state after a delay of latency after the measurement, which is not observable. Latency violates the most fundamental assumption of MDP (Xiao et al., 2020), and thus can cause failure to some RL algorithms.” ” For model-based methods, the planning component is often computationally expensive, and incurs additional latency.”
  • “pretrain a policy network with demonstrations via learning (also called behavioral cloning)”
  • Overfitting may be a cause of worsening learning quality with more experiences.
  • “effective exploration is particularly challenging in tasks with sparse reward. In the most extreme version of this problem, the agent must essentially find a (high-reward) needle in a (zero-reward) haystack. Unfortunately, the most natural formulation of many practical robotics tasks has this property. For this reason, a number of prior works have focused on studying exploration for sparse-reward robotic tasks”
  • A main drawback of Deep RL is the need of massive data.
  • High sensitivity of algorithms, particularly Deep ones, to the initial state and to the way their hyperparameters are set, specially for Off-policy algorithms.
  • “There is a tradeoff here as more environment diversity may cause the policies to have lower performance. Often this can be alleviated with larger and better neural network architectures”
Posted in: Applications of reinforcement learning to robots , Tagged: ,

Deep learning RL methods for robot navigation

June 29, 2023 10:47 ,

Luong, M., Pham, C., Incremental Learning for Autonomous Navigation of Mobile Robots based on Deep Reinforcement Learning, . J Intell Robot Syst 101, 1 (2021) DOI: 10.1007/s10846-020-01262-5.

This paper presents an incremental learning method and system for autonomous robot navigation. The range finder laser sensor and online deep reinforcement learning are utilized for generating the navigation policy, which is effective for avoiding obstacles along the robot’s trajectories as well as for robot’s reaching the destination. An empirical experiment is conducted under simulation and real-world settings. Under the simulation environment, the results show that the proposed method can generate a highly effective navigation policy (more than 90% accuracy) after only 150k training iterations. Moreover, our system has slightly outperformed deep-Q, while having considerably surpassed Proximal Policy Optimization, two recent state-of-the art robot navigation systems. Finally, two experiments are performed to demonstrate the feasibility and effectiveness of our robot’s proposed navigation system in real-time under real-world settings.

Posted in: Applications of reinforcement learning to robots , Tagged: ,

Application of Deep RL to person following by a robot, reducing the training effort of the network by reusing simple state situations in many artificially generated states

September 14, 2021 12:02 ,

Pang, L., Zhang, Y., Coleman, S. et al., Efficient Hybrid-Supervised Deep Reinforcement Learning for Person Following Robot, J Intell Robot Syst 97, 299–312 (2020), DOI: 10.1007/s10846-019-01030-0.

Traditional person following robots usually need hand-crafted features and a well-designed controller to follow the assigned person. Normally it is difficult to be applied in outdoor situations due to variability and complexity of the environment. In this paper, we propose an approach in which an agent is trained by hybrid-supervised deep reinforcement learning (DRL) to perform a person following task in end-to-end manner. The approach enables the robot to learn features autonomously from monocular images and to enhance performance via robot-environment interaction. Experiments show that the proposed approach is adaptive to complex situations with significant illumination variation, object occlusion, target disappearance, pose change, and pedestrian interference. In order to speed up the training process to ensure easy application of DRL to real-world robotic follower controls, we apply an integration method through which the agent receives prior knowledge from a supervised learning (SL) policy network and reinforces its performance with a value-based or policy-based (including actor-critic method) DRL model. We also utilize an efficient data collection approach for supervised learning in the context of person following. Experimental results not only verify the robustness of the proposed DRL-based person following robot system, but also indicate how easily the robot can learn from mistakes and improve performance.

Posted in: Applications of reinforcement learning to robots , Tagged: , ,

A nice (short) survey of deep RL

May 10, 2019 11:43 ,

Matthew Botvinick, Sam Ritter, Jane X. Wang, Zeb Kurth-Nelson, Charles Blundell, Demis Hassabis, Reinforcement Learning, Fast and Slow, Trends in Cognitive Sciences, Volume 23, Issue 5, 2019, Pages 408-422 DOI: 10.1016/j.tics.2019.02.006.

Deep reinforcement learning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go to no-limit poker. This progress has drawn the attention of cognitive scientists interested in understanding human learning. However, the concern has been raised that deep RL may be too sample-inefficient – that is, it may simply be too slow – to provide a plausible model of how humans learn. In the present review, we counter this critique by describing recently developed techniques that allow deep RL to operate more nimbly, solving problems much more quickly than previous methods. Although these techniques were developed in an AI context, we propose that they may have rich implications for psychology and neuroscience. A key insight, arising from these AI methods, concerns the fundamental connection between fast RL and slower, more incremental forms of learning.

Posted in: Psycho-physiological bases of engineering, Reinforcement learning theory , Tagged: ,

Interesting use of RL (deep-RL) for detection – reformulation of detection as a sequential decision process

December 10, 2018 16:15 ,

F. Ghesu et al., Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 1, pp. 176-189, DOI: 10.1109/TPAMI.2017.2782687.

Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and interventional medical image analysis. Current solutions for anatomy detection are typically based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations, including the use of suboptimal feature engineering techniques and most importantly the use of computationally suboptimal search-schemes for anatomy detection. To address these issues, we propose a method that follows a new paradigm by reformulating the detection problem as a behavior learning task for an artificial agent. We couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis. In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body but also how to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. We evaluated our approach on 1487 3D-CT volumes from 532 patients, totaling over 500,000 image slices and show that it significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective, while also achieving a 20-30 percent higher detection accuracy. Most importantly, we improve the detection-speed of the reference methods by 2-3 orders of magnitude, achieving unmatched real-time performance on large 3D-CT scans.

Posted in: Computer vision , Tagged: , ,

Deep reinforcement learning applied to learn both attention and classification in a task of vehicle classification

December 14, 2017 09:21 ,

D. Zhao, Y. Chen and L. Lv, Deep Reinforcement Learning With Visual Attention for Vehicle Classification, IEEE Transactions on Cognitive and Developmental Systems, vol. 9, no. 4, pp. 356-367, DOI: 10.1109/TCDS.2016.2614675.

Automatic vehicle classification is crucial to intelligent transportation system, especially for vehicle-tracking by police. Due to the complex lighting and image capture conditions, image-based vehicle classification in real-world environments is still a challenging task and the performance is far from being satisfactory. However, owing to the mechanism of visual attention, the human vision system shows remarkable capability compared with the computer vision system, especially in distinguishing nuances processing. Inspired by this mechanism, we propose a convolutional neural network (CNN) model of visual attention for image classification. A visual attention-based image processing module is used to highlight one part of an image and weaken the others, generating a focused image. Then the focused image is input into the CNN to be classified. According to the classification probability distribution, we compute the information entropy to guide a reinforcement learning agent to achieve a better policy for image classification to select the key parts of an image. Systematic experiments on a surveillance-nature dataset which contains images captured by surveillance cameras in the front view, demonstrate that the proposed model is more competitive than the large-scale CNN in vehicle classification tasks.

Posted in: Computer vision , Tagged: , , ,