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RL-course-DSR
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# Introduction to (Deep) Reinforcement Learning Here are the exercises for the first/second session of our RL course. ## What You'll Learn This course provides a comprehensive hands-on introduction to Reinforcement Learning (RL) through practical exercises. You'll progress from basic concepts to advanced techniques, building a solid foundation in both theory and implementation. ### Course Structure & Learning Path **🎯 Getting Started** - **Environment Interaction** (`interaction_with_env/`): Learn to create, examine, and interact with RL environments using Gymnasium. Understand observation spaces, action spaces, and basic environment dynamics. - **Your First Agents** (`your_1st_agents/`): Build your first RL agents - from simple random agents to learning agents that improve over time. Master the fundamentals of agent creation, training, and evaluation. **🧠 Core RL Algorithms** - **Monte Carlo Control** (`Monte_Carlo_Control/`): Explore policy evaluation and improvement through Monte Carlo methods. Implement the core components of MC control and understand epsilon-greedy policies. - **Q-Learning** (`Q_learning/`): Implement the foundational Q-learning algorithm from scratch. Learn to update Q-values based on experience and understand the relationship between policies and value functions. **🔧 Advanced Topics** - **Model-Based RL** (`model_based_rl_v0/`): Learn to build and train neural network models of environments. Understand how to use learned models for planning and decision-making. - **Partially Observable Environments** (`partially_observable_env/`): Work with environments where agents have limited information. Implement solutions for CartPole with partial observability using deep RL techniques. - **Custom Environments** (`uneven_maze/`): Create and work with custom RL environments. Explore multi-objective optimization in a maze with uneven terrain. **🛠️ Tools & Frameworks** - **RL Components** (`rlcomponents/`): Build reusable RL components including agents and policies. Learn to structure RL code for maintainability and reusability. ### Key Learning Outcomes By the end of this course, you will be able to: - ✅ Create and interact with RL environments - ✅ Implement fundamental RL algorithms (Q-learning, Monte Carlo) - ✅ Build and train neural networks for model-based RL - ✅ Handle partially observable environments - ✅ Design custom RL environments - ✅ Structure RL code using best practices - ✅ Use modern RL frameworks and tools ### Prerequisites Basic Python programming knowledge and familiarity with machine learning concepts. No prior RL experience required! ## Requirements Create a virtual environment using uv (recommended) or python, e.g. ```bash # Install uv if you haven't already curl -LsSf https://astral.sh/uv/install.sh | sh # Create and activate a virtual environment uv venv source .venv/bin/activate # On macOS/Linux # or .venv\Scripts\activate # On Windows ``` Then install the required packages: ```bash uv sync ``` # Development Setup For development with additional tools like pre-commit: ```bash uv sync --extra dev ``` ## Presentation The presentation of the course can be found [here](https://docs.google.com/presentation/d/1_REcZjt23UiGeazb8a7_g39gUx-7P_riRnj-WMRWAzU/edit?usp=sharing). ## Some references The [old testament bible](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf), God him/her/theirselves. [Deep Reinforcement Learning](http://rail.eecs.berkeley.edu/deeprlcourse/), Sergey Levine. [Introduction to Reinforcement Learning]( https://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver), David Silver. [Reinforcement Learning Virtual School](https://rl-vs.github.io/rlvs2021/), 2021. Finally, the real bible! ## Some places to go ### Environments * [OpenAI Gym environments](https://gym.openai.com/envs/#classic_control) a collection of RL Hello worlds environments. * [A short list of interesting environments](https://medium.com/@mauriciofadelargerich/reinforcement-learning-environments-cff767bc241f) * [A more exhaustive list of environments](https://github.com/clvrai/awesome-rl-envs) * [InstaDeep's Jumanji](https://instadeepai.github.io/jumanji/) ### RL framework * [Stable Baselines](https://stable-baselines.readthedocs.io/en/master/index.html) is a set of improved implementations of Reinforcement Learning (RL) algorithms based on OpenAI Baselines. You can find good papers here! * [RLlib](https://docs.ray.io/en/master/tune/key-concepts.html) is an open-source library for reinforcement learning. * [InstaDeep's Mava](https://github.com/instadeepai/Mava)
