[Mention The Feature]: Add complete tutorial page for UAV Path Planning Algorithm with RL in project page website.

[J-B-Mugundh]

Is your feature request related to a problem? Please describe.

UAV path planning is very crucial and also interesting to learn.

Describe the solution you'd like.

UAV Path Planning Algorithm with RL Problem Definition:

Define the environment (2D/3D space) where the UAV operates, including obstacles, goal locations, and start positions. Identify state variables (e.g., UAV position, velocity, battery level) and actions (e.g., movement directions). State Space and Action Space:

State Space: Represent the environment's state (e.g., position, orientation) in a suitable format (e.g., grid-based, continuous). Action Space: Define possible actions the UAV can take (e.g., move forward, turn left, turn right). Reward Function:

Design a reward function that provides feedback to the UAV: Positive reward for reaching the target. Negative reward for collisions or unnecessary movements. Smaller penalties for energy consumption or time taken. Choose an RL Algorithm:

Select an appropriate RL algorithm, such as Q-learning, Deep Q-Networks (DQN), or Proximal Policy Optimization (PPO), depending on the complexity of the environment and state/action space. Training the Agent:

Initialize the agent with random policies or pre-trained weights. Run simulations in the environment to allow the UAV to explore and learn: For each episode, let the UAV interact with the environment. Update the policy based on the received rewards using the chosen RL algorithm. Policy Improvement:

Continuously refine the policy through episodes: Use experience replay (if applicable) to store and sample past experiences. Adjust exploration strategies (e.g., ε-greedy) to balance exploration and exploitation. Validation and Testing:

Test the trained model in various scenarios to evaluate its performance: Check how well it navigates towards the goal while avoiding obstacles. Assess efficiency (time taken, path length). Deployment:

Once satisfied with the performance, deploy the trained model in real-time scenarios, ensuring it can adapt to dynamic environments if necessary. Continuous Learning:

Implement mechanisms for online learning or retraining in new environments to improve the UAV's adaptability.

Describe alternatives you've considered.

No response

Additional context.

No response

Show us the magic with screenshots

No response

Checklist

• [X] I have checked the Existing Issues
• [X] I have read the Contributing Guidelines
• [X] I want to work on this issue. (optional)

[vivekvardhan2810]

@J-B-Mugundh assigning this issue to you.

Make sure to complete this.

this will be the last issue i am gonna assign you.

As i have said before i will be inactive from tomorrow.

[J-B-Mugundh]

@J-B-Mugundh assigning this issue to you.

Make sure to complete this.

this will be the last issue i am gonna assign you.

As i have said before i will be inactive from tomorrow.

Can I just make one more? Sorry, since I had exams, i couldn't raise issues. I'll complete them for sure

[vivekvardhan2810]

@J-B-Mugundh firstly please complete this issue.

then i will look into other issue.

[J-B-Mugundh]

@J-B-Mugundh firstly please complete this issue.

then i will look into other issue.

Yea sure!

[vivekvardhan2810]

@J-B-Mugundh again that next issue you are gonna raise after you complete this one's.

the next issue will be the last issue that i am gonna assign you.

i hope you got it.

from now on the PR'S are gonna be reviewed by the program manager.