My goal is to become capable of reading research papers and implementing them to solve business/engineering problems using RL. I used the “Study and Learn” mode in ChatGPT to generate the below plan. It seems a decent structure to get me started. I am estimating 5-10 hours per week of work. However, I will push myself to get this done faster and complete it by end of 2025.

Reinforcement Learning Mastery Plan

Time: ~5–10 hrs/week

Month 1: Reinforcement Learning Foundations

Focus: RL terminology, intuition, and multi-armed bandits.

• Set up environment
  • [] Install Python 3.10+ and create a virtual environment.
  • Install core libraries:
    pip install gymnasium[all] stable-baselines3 torch numpy matplotlib
• Read/Watch:
  • Sutton & Barto – Ch. 1 & 2
  • David Silver’s RL Lecture 1
• Implement from scratch:
  • Multi-armed bandit with ε-greedy & UCB strategies.
  • Plot average reward convergence.
• Mini-project:
  • Build a bandit-based recommender system (e.g., news articles).

Month 2: Markov Decision Processes & Tabular RL

Focus: Understanding MDPs, value iteration, and policy iteration.

• Read/Watch:
  • Sutton & Barto – Ch. 3 & 4.
  • David Silver’s RL Lecture 2 & 3.
• Implement from scratch:
  • Value Iteration and Policy Iteration for a GridWorld environment.
  • Visualize value functions and policies.
• Use libraries:
  • Solve FrozenLake in gymnasium using tabular value iteration.
• Mini-project:
  • Create a custom MDP environment (e.g., warehouse robot moving between stations).

Month 3: Temporal Difference Learning

Focus: Q-learning and SARSA.

• Read/Watch:
  • Sutton & Barto – Ch. 6.
  • David Silver’s RL Lecture 4.
• Implement from scratch:
  • Q-learning and SARSA for GridWorld.
  • Experiment with different exploration strategies (ε-decay).
• Use libraries:
  • Train an agent on Taxi-v3 in gymnasium.
• Mini-project:
  • Build an inventory management simulator using Q-learning (decide optimal restocking).

Month 4: Deep Q-Learning

Focus: Function approximation & neural networks for Q-learning.

• Read/Watch:
  • Sutton & Barto – Ch. 9.
  • Deep Q-Learning Explained.
• Implement (scratch):
  • DQN (Deep Q-Network) with experience replay & target networks for CartPole.
• Use libraries:
  • Train a DQN with stable-baselines3 for LunarLander.
• Mini-project:
  • Build a dynamic pricing simulator where an RL agent sets prices to maximize revenue.

Month 5: Policy Gradients (REINFORCE)

Focus: Policy-based methods & their advantages.

• Read/Watch:
  • Sutton & Barto – Ch. 13.
  • David Silver’s RL Lecture 7.
• Implement (scratch):
  • REINFORCE algorithm on CartPole.
• Use libraries:
  • Train policy-gradient agents using stable-baselines3.
• Mini-project:
  • Personalized offer recommendations using policy gradients (contextual bandits).

Month 6: Actor-Critic Methods

Focus: Combining value & policy methods.

• Read/Watch:
  • Sutton & Barto – Ch. 13.
  • Actor-Critic Tutorial.
• Implement (scratch):
  • Actor-Critic for CartPole.
• Use libraries:
  • Train A2C (Advantage Actor-Critic) using stable-baselines3.
• Mini-project:
  • Customer retention simulator – decide discounts to reduce churn.

Month 7: Proximal Policy Optimization (PPO)

Focus: PPO – the modern standard for many RL tasks.

• Read:
  • PPO paper (Schulman et al.).
• Use libraries:
  • Train PPO agents for LunarLander and BipedalWalker.
• Implement (scratch):
  • Build a simplified PPO algorithm to reinforce understanding.
• Mini-project:
  • Workforce scheduling optimization using PPO.

Month 8: Continuous Control – DDPG, TD3, SAC

Focus: Algorithms for continuous action spaces.

• Read:
  • DDPG Paper.
  • Soft Actor-Critic Paper.
• Use libraries:
  • Train DDPG, TD3, SAC on MuJoCo or PyBullet environments.
• Mini-project:
  • Simulated robotic arm for pick-and-place tasks (PyBullet).

Month 9: Exploration & Advanced Techniques

Focus: Advanced exploration & sample efficiency.

• Read:
  • Exploration strategies: curiosity-driven learning, entropy regularization.
• Use libraries:
  • Try Noisy Nets or Curiosity-based exploration agents.
• Mini-project:
  • Warehouse path optimization with curiosity-driven exploration.

Month 10: Multi-Agent RL

Focus: Multiple agents interacting in one environment.

• Read:
  • Multi-Agent RL survey.
• Use libraries:
  • Train agents in PettingZoo environments.
• Mini-project:
  • Supply chain network optimization – agents representing suppliers, warehouses, and retailers.

Month 11: Meta & Hierarchical RL

Focus: Agents that learn to adapt quickly or solve complex tasks in layers.

• Read:
  • Meta-RL overview.
  • Hierarchical RL concepts (options framework).
• Mini-project:
  • Hierarchical RL for complex multi-step tasks (e.g., multi-stage production planning).

Month 12: Research & Real-World Applications

Focus: Reading papers, implementing them, applying RL to real problems.

• Select 1–2 recent RL papers and reproduce key experiments.
• Pick a business problem (e.g., inventory, logistics, personalized recommendations).
• Build an end-to-end RL solution:
  • Define environment & metrics.
  • Train an RL agent with appropriate algorithm.
  • Present results (blog/poster/notebook).

By the end of 12 months:

• I’ll understand classical and deep RL methods.
• I’ll be able to implement key algorithms from scratch.
• I’ll be able to use RLlib & stable-baselines3 for complex tasks.
• I’ll have applied RL to real business/engineering problems.