## 1 Introduction
### The Current State of TD3 Deep Reinforcement Learning  
The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, integrating reinforcement learning and deep learning, identifies optimal policies through deterministic policy gradients and double Q-learning. It comprises a total of six networks: one main Actor network, one target Actor network, two main Critic networks, and two target Critic networks. The Actor network is responsible for receiving states and outputting actions, while the Critic networks receive states and actions and output Q-values. Target networks, possessing the same architecture as the main networks, are utilized to generate target Qvalues, with parameter updates employing a soft update approach. This decoupled design enhances training stability. Model parameter initialization includes the dimensions of the action and state spaces, learning rates, discount factor, and other parameters, calibrating the learning process. Model training encompasses parameter updates of target networks, sample batch retrieval from the experience replay buffer, gradient updates, and loss calculation. Interaction data are stored in the experience replay buffer via an experience replay mechanism, enabling offline learning. TD3 effectively addresses issues such as Q-value overestimation and training instability through mechanisms like target policy smoothing, delayed policy updates, and double Q-learning. It is applicable to Unmanned Surface Vehicle path planning for continuous control tasks in complex environments. [PERSON] et al. (2018) focused on mobile robot path planning, introducing prioritized experience replay and a dynamic delayed update strategy. This approach accelerates learning by prioritizing the selection of experience samples with high TD-errors and dynamically adjusts the update frequency of the Actor network based on the Critic network's update status, thereby improving the algorithm's success rate and training speed. [PERSON] et al. (2018) applied TD3 to drone path planning, integrating deep reinforcement learning with monocular camera data to achieve autonomous path planning without relying on GPS signals or target depth information. [PERSON] et al. (2018) proposed the I-TD3 algorithm, incorporating Prioritized Experience Replay (PER) and an averaged Q-value update strategy. PER enhances the learning efficiency of critical samples through a TD-error-based priority sampling mechanism, while the averaged Q-value update strategy balances Q-value overestimation and underestimation by using the mean of Q1 and Q2 in target value updates, ultimately improving the algorithm's stability. [PERSON] et al. (2018) devised a TD3 algorithm integrated with a safety verification mechanism for real-time obstacle avoidance in dynamic environments for robotic arms. This algorithm mitigates collision risks and planning delays by utilizing incremental path replanning, a deterministic policy, and a simplified state transition function. [PERSON] et al. (2018) introduced APPRL, an adaptive path planning reinforcement learning algorithm based on the TD3 framework, which combines TD3 with the Artificial Potential Field (APF) algorithm. APPRL leverages vector fields generated by APF, combined with actions from the Actor network, as inputs to the Critic network, selecting the action with the higher Q-value as the final output. [PERSON] et al. (2018) developed TD3-IMP for UUV path tracking control, featuring a dual experience replay mechanism based on TD error and average round reward, along with a dynamic policy smoothing method and a dynamic reward function. [PERSON] et al. (2018) proposed a TD3 algorithm based on Hybrid Prioritized Experience Replay (HPER) for USV trajectory tracking in complex marine environments, optimizing policy exploration with a composite reward function and state transition function. [PERSON] et al. (2018) developed TD3-PI, a TD3-based adaptive PI controller, dynamically adjusting PI controller parameters via DRL to address USV motion control challenges under wind and wave disturbances.