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62 lines
4.0 KiB
Markdown
62 lines
4.0 KiB
Markdown
# Towards better explorations methods with Curiosity
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In this Unit, we'll study in theory **Curiosity in Deep Reinforcement Learning**, a technique used to push our agent to better explore its environment and solve two majors flaws in Deep Reinforcement Learning:
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1️⃣ **Sparse rewards environments: environments were most rewards do not contain information, and hence are set to zero.**
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For instance, in [Vizdoom environment](https://github.com/mwydmuch/ViZDoom) “DoomMyWayHome,” your agent is only rewarded if it finds the vest. However, the vest is
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**far away from your starting point, so most of your rewards will be zero**. Therefore, if our agent does not receive useful feedback (dense rewards), it will take much longer to learn an optimal policy and it **can spend time turning around without finding the goal**.
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2️⃣ **The extrinsic reward function (environment reward function) is handmade, that is in each environment, a human has to implement a reward function. But how we can scale that in big and complex environments?**
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Therefore, a solution to these problems is to develop a reward function that is intrinsic to the agent, i.e., generated by the agent itself. The agent will act as a self-learner since it will be the student, but also its own feedback master.
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This intrinsic reward mechanism is known as curiosity because this reward push to explore states that are novel/unfamiliar. **In order to achieve that, our agent will receive a high reward when exploring new trajectories.**
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We'll see two techniques to create this curiosity, both of them are based on a Paper.
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## Required time ⏱️
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The required time for this unit is, approximately:
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- One hour for the first paper study
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- One hour for the second paper study
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## Start this Unit 🚀
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1️⃣ 📖 Read [Curiosity-Driven Learning through Next State Prediction](https://medium.com/data-from-the-trenches/curiosity-driven-learning-through-next-state-prediction-f7f4e2f592fa)
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2️⃣ In addition, you should read the paper 👉 https://pathak22.github.io/noreward-rl/
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3️⃣ 📖 Read [Random Network Distillation: a new take on Curiosity-Driven Learning](https://medium.com/data-from-the-trenches/curiosity-driven-learning-through-random-network-distillation-488ffd8e5938)
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4️⃣ In addition, you should read the paper 👉 https://arxiv.org/pdf/1808.04355.pdf
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## Additional readings 📚
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- [Curiosity and Procrastination in Reinforcement Learning, Google Brain](https://ai.googleblog.com/2018/10/curiosity-and-procrastination-in.html)
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- [ML-Agents, Curiosity for Sparse-reward Environments](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/ML-Agents-Overview.md#curiosity-for-sparse-reward-environments)
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## How to make the most of this course
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To make the most of the course, my advice is to:
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- **Participate in Discord** and join a study group.
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- **Read multiple times** the theory part and takes some notes
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- Don’t just do the colab. When you learn something, try to change the environment, change the parameters and read the libraries' documentation. Have fun 🥳
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- Struggling is **a good thing in learning**. It means that you start to build new skills. Deep RL is a complex topic and it takes time to understand. Try different approaches, use our additional readings, and exchange with classmates on discord.
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## This is a course built with you 👷🏿♀️
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We want to improve and update the course iteratively with your feedback. **If you have some, please fill this form** 👉 https://forms.gle/3HgA7bEHwAmmLfwh9
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## Don’t forget to join the Community 📢
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We have a discord server where you **can exchange with the community and with us, create study groups to grow each other and more**
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👉🏻 [https://discord.gg/aYka4Yhff9](https://discord.gg/aYka4Yhff9).
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Don’t forget to **introduce yourself when you sign up 🤗**
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❓If you have other questions, [please check our FAQ](https://github.com/huggingface/deep-rl-class#faq)
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### Keep learning, stay awesome,
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