پروژه یادگیری تقویتی با پایتون (8346)

شرح پروژه

این رو بفرستین به مجری لطفا و جواب شون رو بفرستین برام

این کارم یادگیری تقویتی هست که کدد هاش با پایتون انجام میشه

ریپورت و  همه چی به انگلیسی باشه  ریپورت با جزیات زیاد میخواهم و فیلم آموزشی که همه مراحل کار رو برام توضیح بدن

اینجا همه توضیحات رو دادن استادم اسم الگوریتم ام PPO هستش و مسأله اش ربات آرم منیپولیشن است شما واسه مجری ام بفرستین خودشون متوجه میشن

Deep Reinforcement Learning (DRL) Project Assignment

 Algorithm : PPO 

Problem : Robot Arm manipulation 

Objectives:

1. Apply the assigned DRL algorithm to the specific problem.

2. Design, train, and test an agent to achieve the target objectives.

3. Analyze the model’s performance and explore improvements through tuning.

4. Document your process thoroughly, covering each phase from problem definition to testing results.

Steps to Complete the Assignment:

1. Algorithm and Problem Review:

- Review the assigned algorithm to understand its architecture, strengths, and common use cases.

- Research any literature that applies similar algorithms to related problems for context and insight.

2. Environment Setup:

- Define or set up the environment related to your problem using frameworks like OpenAI Gym, Unity ML-Agents, or similar tools.

- Implement a reward function that aligns with the problem’s objectives and encourages the desired agent behavior.

3. Algorithm Implementation:

- Implement the provided DRL algorithm, tailoring it to work effectively in the environment for your specific problem.

- Set up initial hyperparameters and configurations, adjusting as necessary to stabilize training.

4. Training and Testing:

   - Train the agent in the environment, tracking its learning progress.

   - Run test episodes to evaluate the agent’s performance against the defined goals.

   - Identify any performance bottlenecks or points where the agent struggles to adapt.

5. Performance Optimization:

   - Experiment with hyperparameter tuning, alternative reward structures, and small adjustments to improve agent performance.

   - Document your tuning experiments and summarize the impact of each change.

6. Results Analysis:

   - Evaluate the final performance of the agent against baseline expectations.

   - Summarize the learning curve, showing any significant improvements or consistent behaviors.

7. Documentation:

   - Written Report: Compose a 6–8 page report covering your methodology, training process, challenges encountered, and results.

   - Code: Submit your code in a clear and organized repository with comments that explain each main component.

Deliverables:

1. Code: Complete code in a well-structured repository.

2. Report: A concise report documenting your approach, analysis, and results.

3. Presentation: Be prepared to give a brief overview of your project’s key insights to the class.

Evaluation Criteria:

1. Technical Accuracy (30%): Correct implementation and application of the algorithm to the given problem.

2. Problem Solving and Tuning (20%): Effectiveness in addressing problem-specific challenges and optimizing model performance.

3. Documentation and Reporting (30%): Clarity and thoroughness in the report, describing methods and analysis.

4. Code Quality (20%): Organization, readability, and quality of the code submitted.

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