An Improved Distributed Sampling PPO Algorithm Based on Beta Policy for Continuous Global Path Planning Scheme

Question

1. What are the limitations of traditional path planning algorithms?

2. How does the Beta policy compare to the Gaussian policy in terms of path planning stability and success rate in different state-spaces?

3. What are the learning tasks in reinforcement learning?

4. What is the overestimation problem in DQN?

Accepted Answer

The limitations of traditional path planning algorithms include reliance on prior knowledge of the system, the need for updated and accurate environmental information for re-planning, lack of consideration for the ship's navigational capability, and inefficient topological search. These algorithms also face challenges in handling increasing complexity in the planning scene. To address these limitations, various approaches have been proposed, such as dynamic trajectory generation models, intelligent collision avoidance navigation methods, and path planning algorithms based on reinforcement learning. However, these methods still face issues with reward function design and incomplete ship propulsion strategies during the planning process. The proposed distributed sampler PPO algorithm aims to overcome these limitations by considering the ship's kinematic model and integrating a reward function that accounts for distance, boundary control, obstacles, and arrival point. This approach enhances sampling performance and enables robust ship path planning in continuous domain spaces.

Accepted Answer

The Beta policy was significantly better than the Gaussian in the path planning stability in different state-spaces. This advantage was shown in the case of testing at the same initial position, where the median of Area1 was nearly 50% higher in the Beta policy (data sequence 171, image 180) than the Gaussian policy (data sequence 329, image 370). The differences between Q3 and Q1 of the Beta policy in Area2, Area4, and Area5 were lower than those of the Gaussian, indicating that the Beta strategy was more stable during the test. Additionally, the planning success rate performance of the Beta policy was higher in all regions, with a success rate higher than 75% in each region. This suggests that the Beta policy provides more stable and successful path planning compared to the Gaussian policy in different state-spaces.

Accepted Answer

In reinforcement learning, learning tasks can be divided into value-based and policy-based categories. Value-based tasks focus on estimating the value of each state or state-action pair, while policy-based tasks aim to directly learn the optimal policy. Both categories are essential for solving global path planning problems. Value-based methods, such as Q-learning, estimate the expected return for each state or state-action pair, allowing the agent to make decisions based on the estimated values. Policy-based methods, like policy iteration and value iteration, directly learn the optimal policy by iteratively improving the policy until convergence. These learning tasks are crucial for the agent's decision-making process in a Markov decision process (MDP), where the agent interacts with the environment and receives feedback through rewards. The agent's goal is to maximize the cumulative reward by selecting the best actions in each state. By understanding the different learning tasks in reinforcement learning, researchers can choose appropriate methods for solving global path planning problems with discrete and continuous action spaces.

Accepted Answer

The overestimation problem in DQN occurs because it performs action-value estimation based on the Bellman Equation, leading to an unavoidable overestimation in the process of action-value Q estimation. This happens because the target network always selects the action that maximizes the action-value Q * (s t , a t ) according to its own policy th t, which leads to bootstrap. However, the online network Q(s t+1 , a; th t ) used for action selection and target calculation may not always correspond to the same action that maximizes the value q-max in the network target due to a delay in the update of the network target. This discrepancy results in overestimation of the action-value Q estimation.

Accepted Answer

The policy gradient-based approach is a reinforcement learning algorithm that optimizes a policy function by fitting a continuous function representing the current task performance metric and using gradient ascent. It has more robust convergence than the action-value-based approach. The policy gradient is expressed as th J(p th ) = S r p (s) A th p th (a | s)Q p (s, a)dads = E s~r p ,a~p th g q (3), where r p (s) is the state distribution, p th (a | s) denotes current state s according to the policy sampled by the parameter th, and g q represents the policy gradient estimator with Q p (s, a) as the target. The policy gradient is estimated by sampling g q in large numbers, so the sample mean of g q converges to its expected value E g q = th J(p th ) when the number of samples is sufficient. However, the policy gradient-based method has problems of falling into local optima and inefficient policy evaluation.

Accepted Answer

Environment design plays a crucial role in deep reinforcement learning by defining the state-space, action space, and reward function. It determines the scene structure, interaction logic, and constraints for each scene element. In the context of the provided section, the environment design involves constructing a simulation environment using Box2D physics engine and Pyglet module for rendering. The plane frame size is set as n * n with a render scale of s, and obstacles are generated at the center with a generation scale of t. The obstacle area is divided into a grid space of k * k, with a grid radius of a. The obstacle radius ranges from 0.2a to 1.5a, and the number of obstacles generated falls within the range k, k^2 - k. To prevent overlapping, an offset is applied to the obstacle generation coordinates, following a uniform distribution U(-n * t * 0.05, n * t * 0.05). This simulation environment increases the ship and endpoint generation range, improving the algorithm's robustness. The hull generation area is covered in orange, while the arrival point generation area is blue, as shown in Figure 1.

Accepted Answer

LIDAR measures distance for obstacle avoidance by using a ship's radius (b) and the effective reflection range (h * b). The horizontal field angle of detection rays (r) is set, and l ray beams are uniformly arranged within the field. If an object truncates a ray, it is considered closing, and the ray identifier (f lag i) is associated with the intercepted object. The distance to the truncation point is calculated using the equation: G = {(f lag 1, ray 1), (f lag 2, ray 2), ..., (f lag l, ray l)}. This data sequence state-space consists of five different sensor states, including the position sensor, which obtains the current vessel's coordinates (x i, y i). The data are normalized, stacked, and formed into consecutive frames by superposition, reducing scale differences and aiding faster convergence to the optimal policy.

Accepted Answer

The image-based state-space processes and fuses image and navigation data by dividing them into two parts: the image part and the navigation part. The image part handles grayscale and normalization, while the navigation part normalizes the data. Features from both parts are stacked to the same dimension. The network then fuses the features of these two parts. The image shape returned from Pyglet rendering RGB data is [3, sn, sn]. The three-channel RGB image is converted into a single-channel grayscale map, and the image size is reduced to [1, 1, x, x] for policymaking. The image-based state-space consists of the current scenario seascape, position vector, and velocity vector. The processed image and navigation feature data are concatenated along the direction of dim = 1, resulting in an overlayed feature data with the shape 1, frame overlay, x, x and the navigation data shape 1, frame overlay * 4.

Accepted Answer

The action space in ship action control is designed with continuous control in angle and propulsion power dimensions. The angle dimension ranges from 0 to 360 degrees, normalized to the interval [0, 1]. The propulsion power dimension considers the effects of thrust derating and fluid resistance, with a maximum value of 70, also normalized to the interval [0, 1]. This design allows the agent to flexibly adjust the thrust angle and power to adapt to the physical characteristics of the ship control in the path planning task, resulting in accurate planning in different scenarios.

Accepted Answer

The reward function design correlates highly with the state-space to enable the agent to learn the expected policy. It is divided into mainline and auxiliary rewards, with the mainline reward focusing on the distance between the ship and the destination. The auxiliary reward considers the effect of arrival point, boundary control, and obstacles on the traveling state. The initial distance between the vessel and the target is set at the initializing stage, and the current distance is recorded during agent interaction. A positive reward is given if the current distance is less than the recorded distance, and a negative reward is returned otherwise. The potential-based reward shaping method is used to prevent abnormal state-shifting and ensure the agent learns the expected policy. The reward function is designed to remap the rendered image onto a plane and calculate the potential field value at each point. The repulsive field generation considers the distance from obstacles, while the gravitational field is based on the arrival point. The potential matrix is normalized and summed to calculate the reward. This design helps the agent learn the robust correlation between state and reward, accelerating policy convergence while maintaining optimal policy invariance.

Accepted Answer

Boundary control in repulsive field generation plays a crucial role in path planning by considering the unknown boundary situation. When an agent's action moves the ship towards the boundary, the penalty gradually increases, ensuring the selected route favors the known and clear center region. This improves the planned route's security. By setting a penalty boundary limit and calculating the bias based on the distance limit, the potential plane is generated. The potential matrices with obstacles, arrival points, and boundary control are superimposed and normalized to obtain the complete potential-based reward function. This approach enhances the agent's ability to navigate safely and efficiently in complex environments.

Accepted Answer

DDPG, a deterministic policy used in continuous domain control path planning, faces several limitations. Firstly, it requires exploration noise during early training to ensure adequate exploration of the environment. However, if the agent fails to obtain sufficient mainline rewards during exploration, DDPG quickly converges to a suboptimal policy, leading to a deadlock state that cannot be overcome by increasing mainline rewards later. This issue is exacerbated by sparse rewards and deterministic policy. Additionally, DDPG suffers from long training times, low exploration efficiency, and unstable learning strategies, resulting in poor reproducibility and unsuitable path planning in complex environments. These limitations highlight the need for alternative approaches to address the challenges in continuous domain control path planning.

Accepted Answer

The PPO algorithm uses clipping to reduce the problem of excessive deviation of the new policy from the old one. It involves the hyperparameter that limits the difference between the two policies. The clipping mechanism ensures that the new policy does not deviate too much from the old policy, maintaining stability during optimization. This is achieved by comparing the ratio of the new policy's probability to the old policy's probability for each action in a given state. If the ratio is greater than a predefined threshold, the update is clipped to prevent excessive changes. This helps in maintaining a balance between exploration and exploitation, ensuring that the policy does not deviate too much from the old policy while still allowing for improvements. Overall, clipping plays a crucial role in the stability and effectiveness of the PPO algorithm's policy optimization process.

Accepted Answer

The feature fusion differs between Actor and Critic networks due to their different output dimensions. The Actor network uses the same feature extraction structure as the Critic network, but the feature fusion varies. The Actor and Critic networks have slight variations depending on the state-space. The Actor network for processing the data sequence state-space has a four-layer fully connected structure with hidden layers of [400, 300, 300]. In contrast, the Actor network for processing the image state-space adds feature extraction layers to extract information from image state. The structure of the Actor in the image state-space is shown in Figure 5. The image feature extraction structure consists of four convolutional layers, with the first three layers using the same convolutional architecture of DQN when processing Atari images [9]. The fourth layer adds a convolutional layer with an output channel of 256, a kernel of 3 * 3, strides of 1, and a padding of 1. The extracted feature is concatenated with the current ship's navigation information and then fed into the fully connected network with a hidden layer of [1124, 500, 300]. The model outputs the mean and variance of Gaussian distribution. The current model is used for global path planning tasks and can learn the spatio-temporal relationship of object motion on consecutive time frames through data frame superposition. To match the continuous data frame superposition structure used in the two state-space designs, multi-frame superposition is considered in the design of both model network structures.

Accepted Answer

The PPO algorithm can lead to insufficient exploration of different areas in images, causing premature termination due to collisions. This results in a lack of samples, making it difficult to train at certain starting positions. Uneven sample acquisition affects the agent's policy learning, as seen in Figure 6a. The policies at related starting points in graphs b, c, e, d, and f are similar to those in graph a, indicating the impact of uneven sample acquisition on policy learning.

Accepted Answer

Distributed sample extraction PPO improves agent policy robustness by using sub-processes to collect data and send them to the main process for optimization. This method allows data from different starting regions to be integrated, sharing data among workers and increasing sample diversity. The experimental results in Section 5.1 show that this improved method significantly enhances the robustness of the agent policy. The distributed sampling policy based on region division balances the number of samples at each initial position through region interaction. Additionally, it obtains several times the data of the PPO algorithm over the same duration, reducing convergence time. The robustness of the algorithm is significantly improved due to the rising number of samples. Figure 6b demonstrates the test results, where the distributed sampling policy with area division achieves better performance compared to the PPO baseline (Figure 6a) in the test of starting positions at equal intervals.

Accepted Answer

The Beta distribution improves action sampling in continuous action space by avoiding the boundary effects caused by the Gaussian distribution. The Beta distribution is a finite-support distribution that does not produce Gaussian-like boundary effects, ensuring unbiased gradient computation and faster convergence. To ensure that the action outputs conform to the different limits in the action space, the Beta policy is defined with parameter c determined based on the action range. The Beta distribution is generated using policy th to estimate the parameters a and b, ensuring a, b > 1 for a concave probability density curve. Experiments have shown that using the Beta distribution leads to faster training speed and higher accumulated reward compared to the Gaussian policy for the same training time. The Beta distribution also allows for remapping the range of the distribution to fit the action space limits, avoiding sampling actions beyond the action boundaries.

Accepted Answer

Potential field remapping plays a crucial role in algorithm performance. In the given section, it is mentioned that rendering the potential field in the range of [80, 160] improved the algorithm performance when the mapping size increased. However, when the remapping parameter 'm' was set above 160, the improvement of the algorithm effect was weakened. Therefore, it was decided to set the potential field remapping parameter to m = 160. This indicates that there is an optimal range for potential field remapping that maximizes algorithm performance. By finding this optimal range, researchers can fine-tune their algorithms to achieve better results in their experiments.

Accepted Answer

In comparing RL and traditional path planning methods, two scenarios were selected with the same initial status. The A* algorithm and RL method were used to compare the planned paths. Figure 11 shows the trajectory of RL as smoother than A* and avoids obstacles effectively. RL considers navigation safety, while A* does not. RL can plan routes with economies and security, unlike A*.

Accepted Answer

The Beta and Gaussian policies were trained in two different dimension state-spaces to compare their performance in path planning. Figure 12 shows the reward with mean curves and error intervals, indicating that the Beta policy had a higher reward than the Gaussian policy in worker0 and worker3 cases during training. However, the Gaussian policy in worker1 and worker2 cases experienced oscillation due to policy decline. The Gaussian policy based on data sequence and image decayed after 100 iterations, while the Beta policy recovered after 400 episodes. The image state-space converged faster than the data sequence state-space due to its comprehensive global information. Overall, the Beta policy demonstrated higher stability and robustness compared to the Gaussian policy.

Accepted Answer

The proposed deep reinforcement learning path planning addresses limitations by designing a generic environment framework for path planning tasks, using the Box2D physics engine to build the environment structure. It implements the environment design for continuous state-space and action space for reinforcement learning. The study solves problems faced by reinforcement learning in path planning tasks, such as unreasonable reward function design, deterministic policies, and incomplete consideration of ship propulsion characteristics. A reward function is designed to match the state-space, considering the distance between the agent and the arrival point as the main part and incorporating potential-based superposition of the arrival point, obstacles, and boundary control as auxiliary parts. The stochastic policy algorithm PPO is employed as a baseline for path planning, enabling the reinforcement learning algorithm to learn from continuous angle control and engine propulsion features simulated by Box2D. A distributed sampling PPO algorithm based on the Beta policy is proposed to address sample collection imbalance in global path planning tasks, improving the algorithm's performance and robustness. The approach enhances the algorithm's performance in terms of accumulated reward and planning success rates across different sub-regions by resolving the action boundary problem associated with Gaussian policy. However, the study has limitations, such as the absence of testing in real marine environments and the lack of comparison with genetic algorithms for path planning. The researchers are currently deploying the algorithm on hardware to assess its performance in sim2real and plan to introduce genetic algorithms into the path planning environment to evaluate their performance.

An Improved Distributed Sampling PPO Algorithm Based on Beta Policy for Continuous Global Path Planning Scheme

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Most frequently asked questions

1. What are the limitations of traditional path planning algorithms?

2. How does the Beta policy compare to the Gaussian policy in terms of path planning stability and success rate in different state-spaces?

3. What are the learning tasks in reinforcement learning?

4. What is the overestimation problem in DQN?

5. What is the policy gradient-based approach?

6. How does environment design impact deep reinforcement learning?

7. How does LIDAR measure distance for obstacle avoidance?

8. How does image-based state-space process and fuse image and navigation data?

9. How is the action space designed in ship action control?

10. How does the reward function design correlate with the state-space?

11. How does boundary control in repulsive field generation affect path planning?

12. What are the limitations of DDPG in continuous domain control path planning?

13. How does PPO algorithm use clipping?

14. What is the feature fusion difference between Actor and Critic networks?

15. How does PPO algorithm affect global path planning in images?

16. How does distributed sample extraction PPO improve agent policy robustness?

17. How does the Beta distribution improve action sampling in continuous action space?

18. How does potential field remapping affect algorithm performance?

19. How do RL and traditional path planning methods compare in planned paths?

20. How do Beta and Gaussian policies compare in path planning?

21. How does the proposed deep reinforcement learning path planning address limitations?

Citations

Guidance Design for Escape Flight Vehicle against Multiple Pursuit Flight Vehicles Using the RNN-Based Proximal Policy Optimization Algorithm

A Novel Dynamically Adjusted Entropy Algorithm for Collision Avoidance in Autonomous Ships Based on Deep Reinforcement Learning

Random Network Distillation Based Deep Reinforcement Learning for AGV Path Planning

An Intelligent Decision-Making Algorithm Based on Reference-Degree Linguistic Fuzzy Set for Path Planning

References

Proximal Policy Optimization Algorithms

Playing Atari with Deep Reinforcement Learning

Deep reinforcement learning with double Q-learning

Policy Gradient Methods for Reinforcement Learning with Function Approximation

Trust Region Policy Optimization

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