Deep Reinforcement Learning in Computer Vision: A Tutor

作者：禅与计算机程序设计艺术

1.简介

Deep Reinforcement Learning (DRL) is a branch of machine learning capable of enabling agents to learn from experience without explicit programming. DRL has become a vital technology for addressing complex challenges across diverse fields such as robotics. It is particularly effective in scenarios involving sequential decision-making under uncertainty. This approach finds applications in various image recognition tasks including object detection and classification; instance segmentation; depth estimation; action recognition; among others. The primary objective in these applications is to identify and comprehend objects or actions through the analysis of visual data.

本研究旨在全面综述计算机视觉及相关领域中基于深度强化学习（DRL）的研究进展。我们将介绍涉及深度强化学习方法的基本概念、算法以及操作步骤，包括图像观测、智能体设计、训练过程、评估机制、探索策略以及模仿学习。此外，本研究还将深入探讨该领域的最新进展与面临的挑战。最后的总结性讨论将关注当前存在的问题并展望未来可能的研究方向

This article requires readers to have prior knowledge of deep learning techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). A background in reinforcement learning terminology and underlying mathematical theory can be helpful but is not mandatory. This document offers a high-level overview of existing works in DRL within computer vision, highlighting the key challenges and current research trends. It provides detailed explanations and illustrations of essential technical components, including neural network architectures, exploration strategies, model-based versus model-free approaches, and reward functions. Readers will find it more straightforward to navigate this text and develop an intuitive understanding of DRL in computer vision alongside its practical applications and limitations.

2.基本概念、术语介绍

In this part of the document, we begin by defining core terminology that will be utilized throughout this article. Subsequently, we delve into how Deep Reinforcement Learning (DRL) techniques are employed in the domain of computer vision through its three foundational principles: observation modeling, policy formulation, and value function approximation. Moving forward, we examine various exploration methods and their significance in enhancing performance during the learning process. The process involves several key steps: data gathering for training examples; designing model architecture tailored to specific tasks; detailing algorithmic components that enable decision-making; and outlining implementation procedures for deploying these systems effectively. Finally, we address common challenges encountered when implementing DRL-based systems within computer vision applications and propose effective solutions to mitigate these issues.

2.1 Terms and Definitions

1. Observation: An observation denotes the input provided to an agent during which it interacts with its surroundings. For instance, in image recognition tasks, an observation can be a portion of an image captured from a video frame at a specific time point.

2. 策略：策略定义了代理体在给定环境中行为的方式，这种定义依赖于代理体的感知输入信息。策略可以是确定性的也可以是随机性的取决于代理体的决策是否完全由其过去的观测结果所决定或者是否存在随机性影响。

Value Function Approximation: A value function approximator accepts an observation and denotes a scalar value signifying the expected return attainable by adhering to a particular policy from that point forward. It is capable of learning the correspondence between observations and associated expected returns, thereby enhancing its precision in estimating rewards.

Exploration strategies define the mechanisms by which agents explore their environment prior to policy optimization. The fundamental objective of exploring uncharted states and actions is to prevent agents from becoming trapped in suboptimal local minima or dead-end scenarios... This practice enhances their ability to generalize across diverse situations. In deep reinforcement learning (DRL), a variety of exploration strategies are employed... including epsilon-greedy approaches... Boltzmann-based methods... Gaussian perturbation techniques... Q-learning enhanced noise injections... multi-armed bandit frameworks... and intrinsic motivation-driven approaches....

Data Collection: The activity of collecting trajectory data experienced by an agent in the real world is referred to as data collection. Typically, this process involves the acquisition of image sequences from videos captured by cameras installed continuously over extended periods. These image sequences serve as the foundation for training datasets used in DRL models within computer vision research.

6. 延拓选择: 人工网络架构的选择对所学表示的表征力和稳定性均具有重要影响，在此过程中使得智能体能够准确预测状态空间并采取最优行动。在计算机视觉领域中用于深度强化学习(DRL)的各种架构中存在多种选择范围从简单的前馈网络到更为复杂的层次式架构如残差网络和变压器。

Algorithm Details: The specific implementation process of DRL involves meticulous attention to detail across multiple key parameters, including the number of updates executed at each time step, the capacity allocated for replay buffers, and the frequency at which model parameters are updated. In this section, we will delve into a detailed examination of these crucial hyperparameter settings.

8. Model-Based vs Model-Free Methods: Traditional RL methods primarily depend on model-based techniques, which approximate the system's dynamics and infer optimal control policies directly from these approximations. In contrast, model-free methods do not require explicit modeling of the environment and instead aim to learn optimal policies by analyzing samples obtained through interactions with the environment. This distinction is particularly pronounced in imitation learning methods, which generally necessitate expert demonstrations to enable knowledge transfer across different environments.

Reward Functions: The aim of an agent interacting with its environment is typically determined by a reward signal. This reward signal tends to stimulate the agent's behavior in accordance with observable features within its surroundings. Reward signals can take various forms, such as sparse or dense binary feedback mechanisms, numerical rewards assigned per event occurrence, and continuous value estimates derived from predictive models trained on historical data.

Batch size parameter: The batch size represents the quantity of experiences sampled during each training iteration to update the agent parameters within a single learning cycle. Utilizing a larger batch size can enhance both the efficiency and convergence speed of model training, albeit at the cost of increased computational demands. Increasing this value typically enhances the convergence speed of model optimization but may require more computational resources. Balancing these factors is crucial for effective learning outcomes.

在训练过程中, 网络的迭代次数代表整个数据集被传递一遍的时间. 减少迭代次数可能导致收敛速度减慢, 在初始学习率过高的情况下尤其明显.

Target Network: The target network keeps a copy of the underlying online network throughout the training process, which is refreshed at regular intervals with the current weights from the online network. This setup plays a crucial role in stabilizing learning by mitigating oscillations that arise from correlations between successive gradient updates.

Experience replay buffers are structures designed to store previously experienced state-action-reward transitions. They act as a source of stochasticity to generate training batches by replaying past experiences. During the training process, newer transitions are discarded to make way for older ones, which are given higher priority due to their potential for better learning outcomes. The priority assigned to each transition depends on the temporal difference error between the predicted discounted future reward and the actual return observed when performing the same action in that state.

2.2 Principles of DRL in Computer Vision

The application of deep reinforcement learning (DRL) in computer vision is fundamentally grounded in four key components: observation mechanisms, policy formulations, approximate value function computations, and exploration strategies. I am now going to delve into the practical application of these principles by examining a typical scenario involving image classification within the framework of object detection.

2.2.1 Observations

An observation represents the data fed into an agent during its interaction with the environment. Typically, image classification in object detection involves processing multiple frames from a camera mounted on an object to detect and classify objects within those frames. Each frame is composed of a sequence of pixels that can be directly provided as input to a Convolutional Neural Network (CNN). Thus, each frame's input representation is referred to as an image patch.

However, another category of observations frequently employed in DRL within computer vision are the optical flow fields, which capture motion patterns between consecutive frames. Optical flows aid the agent in assessing spatial relationships among objects and recognizing movement patterns, thereby facilitating object tracking and adapting to changes in appearance over time. Therefore, DRL within computer vision often integrates image patches and optical flow fields as components of its input representation.

2.2.2 Policies

该策略定义了代理在其感知输入指导下在给定环境中行为的方式。在物体检测任务中，代理接收两个独立的观测结果——一个是图像块（patch），另一个是光流场（optical flow field）。该策略的任务是在这两者之间做出选择：要么仅基于图像块执行物体检测任务，并通过此输出结果完成目标；要么将光流场信息整合到检测流程中以提升定位精度。其中一种常用方法是权衡使用分类头（classification head）还是回归头（regression head）。前者仅对图像块进行二分类判断操作，并输出相应的类别标签；后者则利用光流场信息对已有的边界框位置进行校正调整以获得更精确的结果。

Another key consideration in crafting policies involves how agents engage with their surroundings. Take into account scenarios where prioritizing accuracy over robustness may necessitate compromising on speed or flexibility where applicable. The policy would then tend to emphasize precision over recall while still allowing minor discrepancies from the true ground truth labels. The policy must balance multiple factors while adhering to these constraints using a combination of heuristics and domain-specific insights informed by expert knowledge or risk-averse methodologies as needed.

2.2.3 Value Function Approximation

Value function approximation (VFA) plays a crucial role in deep reinforcement learning (DRL) within computer vision, especially in imitation learning. The expected future returns associated with each state visited by an agent are estimated via VFA. By computing VFA dynamically during training, an agent can adjust its policy based on predicted outcomes from each decision it makes. Similarly, VFA aids in guiding exploration by offering clues about which actions are likely successful and which may not be. However, as previously noted,... model-based approaches like Q-learning.

Recent work in deep reinforcement learning (DRL) within computer vision focuses on integrating advanced deep learning techniques with traditional reinforcement learning algorithms to achieve improved optimization of an agent's policy and value functions. For instance, AlphaZero, an AI system renowned for defeating Stockfish at international chess levels, underwent training by combining supervised learning with self-play. The foundational concept behind AlphaZero lies in merging robust Monte Carlo tree search (MCTS) methodologies with neural networks to establish a universally applicable policy capable of handling intricate chess strategies without the computational burden typically associated with MCTS alone. The resulting AI architecture comprises a hybrid convolutional neural network (CNN)-long short-term memory (LSTM) policy network alongside a mean squared error loss function for comprehensive training.

In general,DRL within the domainofcomputer vision demands a thorough analysisof several critical elements: namely,the selectionof input representation,the architectureof the policy,the exploration strategy,and the methodsfor approximating value functions. Additionally,the rangeofapplications forDRLincorporatesvarious techniques,such asobject detection-scene understanding-event prediction-andtracking.

2.3 Exploratory Strategies

在训练深度强化学习（DRL）代理过程中，探索扮演着至关重要的角色。如果没有探索，在环境中识别相关特征和动作的能力就永远不会被培养出来；这将导致训练过程中显著的成功率水平。在DRL中使用了多种探索策略；这些策略旨在鼓励在训练初期进行广泛探索，并随着时间的推移逐步减少对纯开发性策略的偏好。以下是一些广泛采用的探索策略：包括贪婪策略（epsilon-greedy）、玻尔兹曼探索、高斯噪声注入、Q学习噪声注入、多臂老虎机探索以及内在动机探索。

Epsilon-Greedy Exploration Strategy

The epsilon-greedy strategy involves selecting an action randomly with a probability of ε, or choosing the action that maximizes the expected reward otherwise. Here, ε represents a hyperparameter that determines the extent of exploration. At the beginning of training, ε is set to a relatively high value to encourage extensive exploration of the environment. As learning progresses, this value can be adjusted over time. Once trained, during test phase actions are selected based purely on their expected rewards. This approach ensures efficient exploration by actively seeking out unknown areas and avoiding local optima by proactively probing unexplored regions.

Boltzmann Exploration Strategy

Boltzmann exploration produces a soft probability distribution over possible actions, akin to Thompson sampling, selecting an action proportionally based on its current temperature. Our temperature hyperparameter regulates the extent of exploration: higher temperatures foster more extensive exploration, while lower temperatures diminish it. At elevated temperatures, the agent selects the action with the highest probability; conversely, at extremely low temperatures, all actions are equally probable. The advantage of Boltzmann exploration lies in its ability to avoid the curse of dimensionality and efficiently discover diverse solutions. However, since each action's effect must be evaluated individually, it may occasionally encounter local minima or become trapped in narrow passages. Despite these limitations, Boltzmann exploration remains a potent strategy for exploration in imitation learning within computer vision.

Gaussian Noise Injection Exploration Strategy

The exploration strategy based on Gaussian noise injection introduces small random perturbations into the policy output during testing by drawing samples independently from a standard normal distribution with zero mean and unit variance. This approach ensures that the intelligent agent remains robust against abrupt environmental changes and avoids entrapment in local minima. Additionally, Gaussian noise injection enables effective exploration of non-local regions within the state space, particularly those corresponding to low-probability transitions. Furthermore, this method helps prevent agents from being trapped in regimes characterized by persistent errors, which may result from improper parameter initialization or flawed reward mechanisms.

Q-Learning Noise Injection Exploration Strategy

Q-learning noise injection is a modification of standard Q-learning that injects additional noise into the update rule to encourage exploration and prevent overfitting. Specifically, the agent modifies the Q-value update rule to add a normally distributed random term to the Q-function estimate, which corresponds to adding noise to the action-value estimate. The injected noise follows a fixed schedule, with smaller magnitudes starting late in the training process and increasing to maximum strength later on. This allows the agent to escape local minima and explore the entire state space more thoroughly, potentially finding better solutions. While Q-learning noise injection has been shown to be effective in some domains, it has yet to be compared systematically with other exploration strategies and evaluated on real-world tasks. Nonetheless, Q-learning noise injection remains a valuable technique for exploring large state spaces.

Multi-Arm Bandit Exploration Strategy

The process of multi-arm bandit exploration entails assigning a small portion of the available budget to each individual arm within the multi-armed bandit framework, selecting them uniformly at random. The agent monitors the cumulative rewards accumulated for each arm and selects the arm offering the highest reward until all remaining resources are exhausted. Once interaction with the environment concludes, the agent transitions to a greedy policy that consistently selects the arm with the highest recorded reward. While this approach excels in scaling to larger state spaces and enabling adaptive exploration strategies, it has a notable drawback: it may overlook certain regions of the state space, potentially resulting in inefficient exploration and slower convergence rates.

Intrinsically Motivated Exploration Strategy

Intrinsically motivated exploration targets identifying useful skills and behaviors within an environment through analysis of an agent's internal representations and preferences. By evaluating actions based on immediate rewards and anticipated future rewards under current beliefs, an agent adjusts its understanding of reality as external conditions change, thereby enhancing exploration of previously uncharted state regions. This approach benefits from existing tools such as GPT-3 and OpenAI CLIP, which analyze raw pixel data and generate descriptive captions. Additionally, it can be combined with model-based techniques like Bayesian filtering and MDP solvers to maintain probabilistic environmental models for future-oriented planning. Despite its potential, intrinsically motivated exploration remains a relatively new area of study within computer vision's domain of deep reinforcement learning.