RL:projects - chunhualiao/public-docs GitHub Wiki

https://github.com/chunhualiao/reinforcement-learning-projects // stored here

DeepSeek-R1 Suggestion

Here’s a curated list of Python reinforcement learning (RL) projects, ordered from foundational to advanced, designed to help you learn RL incrementally. All projects are feasible on an M3 MacBook Air with 24GB RAM, assuming efficient code and lightweight frameworks:

1. Classic Multi-armed Bandit Problem

Goal: Implement exploration vs. exploitation strategies.
Concepts: ε-greedy, Upper Confidence Bound (UCB), Thompson Sampling.
Tools: NumPy, Matplotlib.
Project: Simulate bandits with different reward distributions and compare strategies.

2. GridWorld with Tabular Q-Learning

Goal: Solve a grid-based navigation task.
Concepts: Q-learning, SARSA, state-action-reward tables.
Tools: Python (no frameworks needed).
Project: Train an agent to navigate a 5x5 grid to reach a goal while avoiding pits.

3. CartPole with Deep Q-Networks (DQN)

Goal: Balance a pole using a neural network.
Concepts: DQN, experience replay, target networks.
Tools: OpenAI Gym, PyTorch/TensorFlow.
Project: Start with a simple DQN, then add improvements like Double DQN or prioritized replay.

4. Custom Environment: Maze Solver

Goal: Build a custom RL environment.
Concepts: Environment design, state/action spaces, rewards.
Tools: Gymnasium (OpenAI Gym fork), PyGame.
Project: Create a maze and train an agent using Q-learning or DQN.

5. Flappy Bird Clone with DQN

Goal: Use pixel inputs (images) as states.
Concepts: CNN-based DQN, frame stacking, preprocessing.
Tools: PyGame, PyTorch, OpenCV.
Project: Train an agent to play Flappy Bird using raw pixel data.

6. Policy Gradients: LunarLander

Goal: Learn policy-based methods.
Concepts: REINFORCE, Advantage Actor-Critic (A2C).
Tools: OpenAI Gym, PyTorch.
Project: Train an agent to land a spacecraft on the moon.

7. Proximal Policy Optimization (PPO) for CarRacing

Goal: Master state-of-the-art RL algorithms.
Concepts: PPO, continuous action spaces.
Tools: Stable Baselines3, Gymnasium.
Project: Train a car to navigate a track using pixel inputs.

8. Multi-Agent RL with PettingZoo

Goal: Experiment with multi-agent systems.
Concepts: Cooperative/competitive agents, MARL.
Tools: PettingZoo, RLlib (or Stable Baselines3).
Project: Solve tasks like "Pistonball" or "Cooperative Pong."

9. Stock Trading Bot with RL

Goal: Apply RL to real-world financial data.
Concepts: Reward engineering, time-series states.
Tools: Pandas, PyTorch, Yahoo Finance API.
Project: Train an agent to maximize portfolio value using historical stock data.

10. Atari Breakout with Rainbow DQN

Goal: Combine advanced DQN extensions.
Concepts: Rainbow DQN (Noisy Nets, Distributional RL, etc.).
Tools: Gymnasium (Atari), PyTorch.
Project: Reproduce Rainbow DQN results on Atari games.

11. Robotics Simulation with MuJoCo

Goal: Control continuous robotic systems.
Concepts: SAC (Soft Actor-Critic), TD3.
Tools: MuJoCo (free license), Gymnasium, PyTorch.
Project: Train a robot to walk using the Ant or Humanoid environment.

12. Meta-Learning for Fast Adaptation (MAML + RL)

Goal: Learn to adapt to new tasks quickly.
Concepts: Model-Agnostic Meta-Learning (MAML).
Tools: PyTorch, custom RL environments.
Project: Train an agent to solve unseen grid mazes with minimal samples.

13. AlphaZero Clone for Chess

Goal: Implement self-play and Monte Carlo Tree Search (MCTS).
Concepts: MCTS, policy-value networks, self-play.
Tools: PyTorch, Python-Chess library.
Project: Build a simplified AlphaZero for chess or Tic-Tac-Toe.

Tips for Success

Start with small-scale environments (e.g., GridWorld) to debug quickly.
Use PyTorch for flexibility or Stable Baselines3 for pre-built algorithms.
Optimize for macOS: Use MLX (Apple's ML framework) for GPU acceleration on M3.
Monitor memory usage with lightweight batches (e.g., small replay buffers).

By progressing through these projects, you’ll build a strong foundation in RL while tackling increasingly complex challenges.

Incrementally Advanced Hands-on Projects for Learning Reinforcement Learning

This is a list of progressively more advanced reinforcement learning (RL) projects, suitable for beginners and feasible on a MacBook Air M3 with 24GB RAM.

1. Grid World Navigation (Beginner)

Project: Implement a Q-learning or SARSA agent to navigate a simple grid world to reach a goal. This is similar to the rl_gridworld.py program we just created.
Concepts: Introduces basic RL concepts like states, actions, rewards, Q-table, exploration vs. exploitation, and tabular methods.
Complexity: Very low. Computationally inexpensive and easy to visualize.
Example: The rl_gridworld.py program itself is a great starting point. Modify the grid size, reward structure, or add obstacles to make it slightly more challenging.

2. Frozen Lake Environment (Beginner - Intermediate)

Project: Use OpenAI Gym (now Gymnasium) to solve the Frozen Lake environment. Navigate a slippery frozen lake to find a goal without falling into holes.
Concepts: Introduces OpenAI Gym/Gymnasium, environment stochasticity (slippery ice), and still uses tabular Q-learning or SARSA.
Complexity: Low to moderate. Slightly more complex than a deterministic grid world due to stochasticity.
Tools: gymnasium (install with pip install gymnasium).
Next Step: After mastering basic Q-learning, try implementing variations like SARSA or Expected SARSA in Frozen Lake.

3. Taxi-v3 Environment (Intermediate)

Project: Solve the Taxi-v3 environment from Gymnasium. A taxi agent needs to pick up and drop off passengers at different locations.
Concepts: More complex state space, requires better state representation, and is still solvable with tabular methods or potentially function approximation (e.g., tile coding). Introduces more complex reward structures and environment dynamics.
Complexity: Moderate. Requires more thought about state representation and potentially slightly more advanced algorithms.
Tools: gymnasium.
Next Step: Explore function approximation methods if tabular Q-learning becomes inefficient due to the larger state space.

4. CartPole Environment (Intermediate)

cart pole

Project: Solve the CartPole environment from Gymnasium. The goal is to balance a pole on a cart by moving the cart left or right.
Concepts:
- Introduces continuous state space (pole angle and velocity, cart position and velocity) but discrete action space (left or right).
- Often solved with Deep Q-Networks (DQN) or policy gradient methods, but simpler versions can be solved with discretized states and tabular methods or basic neural networks for function approximation.
Complexity: Moderate. Introduces continuous states and the need for function approximation for more efficient learning in continuous spaces.
Tools: gymnasium, TensorFlow or PyTorch for neural networks (if using function approximation).
Next Step: Implement a basic neural network to approximate the Q-function instead of using a Q-table.

5. MountainCar Environment (Intermediate - Advanced)

Project: Solve the MountainCar environment from Gymnasium. The goal is to drive an underpowered car up a steep mountain.
Concepts:
- Continuous state and action spaces.
- Requires more sophisticated RL algorithms like Deep Q-Networks (DQN), Policy Gradients (e.g., REINFORCE, Actor-Critic methods like A2C/A3C), or Proximal Policy Optimization (PPO).
- Highlights challenges of exploration in continuous action spaces.
Complexity: Moderate to Advanced. Requires understanding of function approximation, potentially deep neural networks, and more advanced RL algorithms.
Tools: gymnasium, TensorFlow or PyTorch, libraries like stable-baselines3 (if you want to use pre-implemented algorithms).
Next Step: Experiment with different deep RL algorithms like DQN, PPO, or Actor-Critic methods to solve MountainCar.

6. Atari Games - Breakout or Pong (Advanced - Optional)

Project: Implement a DQN agent to play simple Atari games like Breakout or Pong using Gymnasium's Atari wrappers.
Concepts:
- Reinforcement learning with visual input (pixels).
- Deep convolutional neural networks (CNNs) for feature extraction.
- Handling high-dimensional state spaces.
- Introduces experience replay and target networks in DQN.
Complexity: Advanced. Computationally more intensive, requires understanding of CNNs and more complex DQN implementations. However, simpler versions of Atari environments and smaller networks can be run on a MacBook Air M3.
Tools: gymnasium with Atari wrappers, TensorFlow or PyTorch, libraries like stable-baselines3.
Note: For Atari, start with simplified environments (e.g., using Gymnasium's wrappers to reduce image size and complexity) and smaller neural networks to ensure smooth execution on your laptop.

General Tips for Learning by Doing

Start Simple: Begin with Grid World and Frozen Lake to grasp the fundamentals before moving to more complex environments.
Iterate and Experiment: For each project, don't just aim to solve it. Experiment with different algorithms, hyperparameters, and network architectures to understand their impact.
Visualize and Debug: Visualize the agent's behavior and the learning process. Use debugging tools to understand what's happening inside your RL algorithms.
Use Libraries: Leverage libraries like gymnasium and stable-baselines3 to save time on environment setup and algorithm implementation, especially for more complex projects.
Focus on Understanding: Prioritize understanding the underlying RL concepts and algorithms rather than just getting code to run.

A MacBook Air M3 with 24GB RAM is well-suited for projects 1-5 and even simplified versions of project 6. Start with project 1 and gradually work your way up as you become more comfortable with the concepts and tools.