Learn how to select, explain, display, and promote your Reinforcement Learning projects to impress your target audience.

In the future, I will craft a compelling Reinforcement Learning (RL) portfolio that showcases my proficiency in tackling diverse challenges across multiple domains. Each project will feature well-defined problem statements, detailed implementation insights, and clean, well-documented code hosted on GitHub. I will prioritize performance metrics to demonstrate the effectiveness of my RL models and incorporate visualizations like learning curves and animations for a more engaging presentation. My portfolio will not only highlight technical skills but also convey the learning journey, challenges overcome, and collaborative aspects when applicable.

If opting for creating your own environments, take a look at the Blender Python module "bpy". It can be a little fiddly but has a vast range of objects, constraints, physics and animation options. For example I was able create a simple model of a crane aboard a ship and demonstrate motion compensation with a DDQN in a short 2 week project.

StarCraft is a preferred option for demonstrating reinforcement learning (RL) skills due to its complexity, real-time nature, rich environment, and research significance. Its highly complex real-time strategy gameplay allows you to showcase advanced decision-making and adaptability. The game offers diverse scenarios to explore various RL techniques, demonstrating versatility and creativity. StarCraft has a significant presence in the RL research community, providing opportunities for collaboration and contribution. However, project selection should align with individual goals, interests, and target audience to effectively showcase expertise and passion.

Last updated on Jul 16, 2024

How do you create a Reinforcement Learning portfolio that stands out?

Reinforcement Learning (RL) is a branch of machine learning that focuses on learning from trial and error, rewards and penalties, and interaction with dynamic environments. It has many applications in robotics, gaming, finance, and more. But how can you showcase your RL skills and projects to potential employers or clients? In this article, we will share some tips on how to create a RL portfolio that stands out.

1 Choose your projects wisely

The first step to creating a RL portfolio is to select the projects that demonstrate your knowledge, creativity, and problem-solving abilities. You can choose from existing RL benchmarks, such as OpenAI Gym, Atari games, or MuJoCo, or you can create your own custom environments and tasks. The key is to choose projects that are relevant to your goals, interests, and target audience. For example, if you want to work in robotics, you can show how you trained a simulated robot to perform tasks like grasping, walking, or manipulation. If you want to work in gaming, you can show how you developed an agent that can play complex games like StarCraft or Dota 2.

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Andy Whitworth

Data Scientist | Machine Learning | Python | Chartered Engineer
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If opting for creating your own environments, take a look at the Blender Python module "bpy". It can be a little fiddly but has a vast range of objects, constraints, physics and animation options. For example I was able create a simple model of a crane aboard a ship and demonstrate motion compensation with a DDQN in a short 2 week project.

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Parham MohammadAlizadeh

AI/Software Engineer at Qualisflow
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StarCraft is a preferred option for demonstrating reinforcement learning (RL) skills due to its complexity, real-time nature, rich environment, and research significance. Its highly complex real-time strategy gameplay allows you to showcase advanced decision-making and adaptability. The game offers diverse scenarios to explore various RL techniques, demonstrating versatility and creativity. StarCraft has a significant presence in the RL research community, providing opportunities for collaboration and contribution. However, project selection should align with individual goals, interests, and target audience to effectively showcase expertise and passion.

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Pablo Sánchez

Ing. Civil Electrónico | Machine Learning | Mejora Continua
(edited)
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Mostrar casos de uso simples y escalables, considerando 3 conceptos clave sobre los que descansa, a)Aprendizaje No supervisado, b)Considera un proceso(ambiente), un controlador (agente), 3 variables (estados observados, acciones a aplicar, recompensa recibida desde ambiente) y c) Una GPU para aplicar en forma práctica. El resultado, una red neuronal entrenada (agente aprendio a generalizar sus acciones en ese ambiente ) y que sirve para aplicar en el ambiente donde fue entrenado el agente. Elija el problema(ambiente) y sus dimensiones darán la directriz acerca de cuales y cuantas variables se necesitan para entrenar y la cantidad de computo (nro de GPUs) para que sea aplicable.

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2 Explain your methods and results

The second step to creating a RL portfolio is to explain your methods and results clearly and concisely. You should leverage a combination of text, code, images, videos, and interactive demos to showcase your work. Your portfolio should include the motivation and objective of your project, the RL algorithm and framework you used, the environment and agent specifications, the hyperparameters and evaluation metrics, the challenges and limitations you faced, the results and insights you obtained, as well as future work and improvements you suggest.

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3 Use a suitable platform

The third step to creating a RL portfolio is to use a suitable platform to host and share your work. You can use a personal website, a blog, a GitHub repository, or a platform like Medium, Kaggle, or Colab. The platform you choose should allow you to display your code, data, models, and outputs in an accessible and attractive way. You should also make sure that your portfolio is easy to navigate, update, and link to other sources. You can use tools like Jupyter notebooks, Streamlit, or Dash to create interactive and web-based applications for your projects.

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4 Showcase your skills and personality

The fourth step to creating a RL portfolio is to showcase your skills and personality. You can do this by adding some elements that make your portfolio stand out. For instance, a catchy title and introduction can capture the attention of your audience. Additionally, using humor, anecdotes, or stories can engage your viewers. Visualizations, animations, or sound effects can also be used to enhance your outputs. Furthermore, feedback, comments, or ratings can help you solicit input from your audience. Finally, badges, certificates, or awards can highlight your achievements while testimonials, references, or endorsements can build your credibility.

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5 Update your portfolio regularly

The fifth step to creating a RL portfolio is to update your portfolio regularly. You can do this by adding new projects, improving existing ones, or learning new skills and techniques. You can also update your portfolio by participating in competitions, challenges, or hackathons related to RL. Updating your portfolio will show your passion, curiosity, and commitment to RL. It will also help you keep up with the latest trends, developments, and opportunities in the field.

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6 Promote your portfolio effectively

The sixth and final step to creating a RL portfolio is to promote your portfolio effectively. You can do this by sharing your portfolio with your network, social media, online communities, or potential employers or clients. You can also use keywords, hashtags, or tags to optimize your portfolio for search engines and platforms. Promoting your portfolio will help you reach a wider audience, generate feedback, and create opportunities for collaboration or employment.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Mohammed Bahageel

Artificial Intelligence Developer |Data Scientist / Data Analyst | Machine Learning | Deep Learning | Data Analytics |Reinforcement Learning | Data Visualization | Python | R | Julia | JavaScript | Front-End Development
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In the future, I will craft a compelling Reinforcement Learning (RL) portfolio that showcases my proficiency in tackling diverse challenges across multiple domains. Each project will feature well-defined problem statements, detailed implementation insights, and clean, well-documented code hosted on GitHub. I will prioritize performance metrics to demonstrate the effectiveness of my RL models and incorporate visualizations like learning curves and animations for a more engaging presentation. My portfolio will not only highlight technical skills but also convey the learning journey, challenges overcome, and collaborative aspects when applicable.

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How do you create a Reinforcement Learning portfolio that stands out?

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1 Choose your projects wisely

2 Explain your methods and results

3 Use a suitable platform

4 Showcase your skills and personality

5 Update your portfolio regularly

6 Promote your portfolio effectively

7 Here’s what else to consider

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