Hi everyone,

I’m a CS student aiming to master Reinforcement Learning (RL) for industry roles and startup building. I’ve designed the following roadmap and would really appreciate feedback from experienced practitioners.

My background:

• Comfortable with Python, NumPy, Pandas
• Basic ML & Deep Learning knowledge
• Long-term goal: RL Engineer / Agentic AI systems

🛣️ My RL Roadmap

1️⃣ Foundations

• Python (OOP, decorators, multiprocessing)
• Math: Linear Algebra, Probability, Calculus
• Markov Processes (MDP, Bellman equations)

2️⃣ Classical RL

• Multi-armed bandits
• Dynamic Programming
• Monte Carlo methods
• Temporal Difference (TD)
• SARSA vs Q-Learning

3️⃣ Function Approximation

• Linear approximation
• Feature engineering
• Bias–variance tradeoff

4️⃣ Deep Reinforcement Learning

• Neural Networks for RL
• DQN (experience replay, target networks)
• Policy Gradient methods
• Actor–Critic (A2C, A3C)
• PPO, DDPG, SAC

5️⃣ Advanced RL

• Model-based RL
• Hierarchical RL
• Multi-agent RL
• Offline RL
• Exploration strategies

6️⃣ Tools & Frameworks

• Gym / Gymnasium
• Stable-Baselines3
• PyTorch
• Ray RLlib

7️⃣ Projects

• Custom Gym environments
• Game-playing agents
• Robotics simulations
• Finance / scheduling problems