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Reinforcement Learning Roadmap

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My journey into understanding machine learning began with a 12 week learning syllabus that chatGPT prepared for me. That lead me to the Coursera Deep Learning Specialization which I just completed. The quality of Coursera specialization was excellent and I have a good understanding of the foundations of deep learning now. What began as a 12 week initiative to get a bit of understanding of machine learning has led me down a deep rabbit hole.

Somewhere along the line in looking at all the various branches of machine learning I stumbled across reinforcement learning. The functionality in todays LLMs is truly amazing and you can do great things with these tools but they're not the holy grail of advanced general intelligence (AGI). Reinforcement learning appeals to me because it is somewhat modeled on how humans learn, it can be run with far less computing resources and it aims to tackle AGI. Some of the world leading research comes out of Richard Sutton's group's work at the University of Alberta. The Alberta Plan lays out a plan to advance the field over the next 5-10 years which lines up well with my intentions to embark on a Doctor of Engineering degree. To that end I'm going to dive deeper into reinforcement learning as a potential specialization area.

In a happy coincidence University of Alberta has a Coursera specialization on Reinforcement Learning that I can take. My goal over the next 4 months is to complete the Hugging Face Deep Reinforcement Learning Course, the University of Alberta Reinforcement Learning Specialization and build a strong engineering portfolio demonstrating practical RL implementation skills.

4-Month RL Engineering Roadmap (~10 hrs/week)

Cadence: ~10 hrs/week × 16 weeks
Stack: Python, NumPy, PyTorch, Gymnasium, Stable-Baselines3, Hugging Face Hub, Weights & Biases, Docker, AWS/GCP, pytest, MLflow
Focus: Production-ready code, automated testing, CI/CD pipelines, engineering best practices

Table of Contents

Phase 1: Foundations + Engineering Setup (Weeks 1–4)

Week Core RL Learning (6 hrs) Engineering Skills (2 hrs) Specific Deliverable (2 hrs)
1 C1 M1–M3: Bandits, MDPs; S&B Ch.1–3, HF U0 setup Create rl_toolkit/ Python package with setup.py, implement EpsilonGreedyBandit class Working bandit agent you can install with pip install -e . and run rl-toolkit train --env bandit
2 C1 M4–M5: DP (Policy/Value Iteration); S&B Ch.4, HF U1 Add PolicyIteration and ValueIteration classes with pytest tests Command rl-toolkit solve-gridworld --method policy-iteration that outputs optimal policy
3 C2 M1–M3: MC + TD(0); S&B Ch.5–6, HF U2 Write Dockerfile, add MonteCarloAgent with integration tests docker run rl-toolkit train --env blackjack --agent monte-carlo works and saves results
4 n-step TD; S&B Ch.7; Random Walk experiments Setup GitHub Actions to run tests + generate coverage report PR automatically runs tests, posts coverage %, and benchmarks performance vs main branch

Math (2-3 hrs/week): 3Blue1Brown Linear Algebra + Khan Academy Probability basics

Phase 2: Control & Production Skills (Weeks 5–8)

Week Core RL Learning (5-6 hrs) Engineering Skills (3-4 hrs) Specific Deliverable (1-2 hrs)
5 C2 M4–M5: SARSA, Q-Learning, Dyna-Q; HF U1 CartPole Build rl-toolkit benchmark command that runs SARSA vs Q-Learning with statistical tests Script that outputs "Q-Learning beats SARSA on CartPole-v1 (p<0.05)" with confidence intervals
6 C3 M1–M3: Function Approximation, tile coding; HF U2 DQN Add MLflow tracking to log hyperparameters, metrics, and model artifacts mlflow ui shows experiments with filterable runs and downloadable trained models
7 C3 M4: Control with approximation; HF U3 Atari Create rl-toolkit serve command using FastAPI that loads saved models curl localhost:8000/predict -d '{"state": [1,2,3,4]}' returns action predictions
8 Off-Policy + Eligibility Traces; Ch.11–12 Add memory profiling and GPU utilization monitoring to training Training logs show "Memory: 2.1GB, GPU: 78%, ETA: 5min" with automatic early stopping on plateau

Math (2-3 hrs/week): Applied optimization, gradient descent intuition, matrix operations in practice
Industry Reading: Focus on RL in recommendation systems, robotics, trading

Phase 3: Capstone + Deployment (Weeks 9–12)

Week Core RL Learning (4-5 hrs) Engineering Skills (4-5 hrs) Specific Deliverable (1-2 hrs)
9 C4 M1–M4: Capstone Design; HF U3 Advanced DQN Write integration tests that spin up full training pipeline pytest tests/test_integration.py trains agent for 100 episodes and validates final performance >threshold
10 C4 M5: Implementation with ≥3 seeds; HF U4 Policy Gradients with PyTorch Add rl-toolkit tune command using Optuna for hyperparameter search Command runs 50 trials and outputs "Best params: lr=0.001, batch_size=64 (score: 450 ± 23)"
11 C4 M6: Analysis and optimization; Advanced DQN techniques Deploy API to cloud with health checks and logging Live URL like yourname-rl-api.herokuapp.com/health returns training status and model metrics
12 Capstone completion; HF U5 Unity ML-Agents environments Add load testing with locust that simulates 100 concurrent prediction requests Report shows "API handles 500 req/sec with 95th percentile latency <100ms"

Math (2-3 hrs/week): Statistics for A/B testing, confidence intervals, performance metrics
Industry Reading: Case studies of RL deployment in production

Phase 4: Advanced Methods + Industry Applications (Weeks 13–16)

Week Core RL Learning (4-5 hrs) Engineering Skills (4-5 hrs) Specific Deliverable (1-2 hrs)
13 DQN variants (Double, Dueling); HF U2/U3 review Add property-based tests using Hypothesis library Tests that generate random valid states/actions and verify agent properties never crash
14 HF U8 Proximal Policy Optimization (PPO) with Doom Implement distributed training across multiple GPUs/machines rl-toolkit train --distributed --nodes 2 trains on multiple machines and aggregates results
15 HF U6 Actor-Critic Methods with Robotics Environments Create A/B testing framework comparing algorithms Dashboard showing "Algorithm A vs B: +15% sample efficiency (p=0.02)" with visualizations
16 HF U7 Multi-Agent RL and AI vs AI competition Package everything into pip-installable library pip install your-rl-toolkit lets others run all your agents with simple commands

Math (2-3 hrs/week): Information theory basics, KL divergence, entropy in RL context
Industry Reading: Recent industry applications, ROI studies, deployment challenges

Applied Math Track (2-3 hrs/week)

Focus: Practical application rather than theoretical depth

Weeks 1-4: Foundations - 3Blue1Brown – Essence of Linear Algebra (focus on matrix operations, eigenvectors in practice) - Khan Academy probability exercises (focus on distributions, expectation) - StatQuest – Probability & Bayes

Weeks 5-8: Optimization & Statistics - Gradient descent variations and practical considerations - Hyperparameter optimization methods - Statistical significance testing for ML

Weeks 9-12: Applied Statistics - A/B testing methodology for RL experiments - Confidence intervals and error analysis - Sample efficiency metrics

Weeks 13-16: Information Theory - Entropy and information gain (for exploration strategies) - KL divergence (for policy optimization) - Practical applications in RL algorithms

Milestone: By week 16, should be able to: - Implement gradient-based optimization from scratch - Design statistically valid RL experiments - Explain mathematical concepts behind RL algorithms in engineering terms

Industry Paper Reading (1-2 hrs/week)

Focus: Real-world applications and deployment lessons

Weeks 1-4: Foundations & Case Studies - Industry survey papers on RL applications - Case studies from major tech companies (Google, Meta, Microsoft)

Weeks 5-8: Production Challenges - Papers on RL deployment challenges and solutions - Scalability and efficiency improvements

Weeks 9-12: Domain Applications - RL in recommendation systems - RL in robotics and autonomous systems - RL in financial trading and optimization

Weeks 13-16: Cutting-edge Applications - Recent industry applications and success stories - ROI studies and business impact assessments - Future trends and opportunities

Coverage Maps

Coursera Module Coverage (100%)

Course / Module Scheduled Week(s)
C1. Fundamentals of RL
M1 Welcome W1 (quick pass)
M2 Intro to Sequential Decision-Making (bandits) W1
M3 Markov Decision Processes W1
M4 Value Functions & Bellman Equations W1–W2
M5 Dynamic Programming W2
C2. Sample-based Learning Methods
M1 Welcome W3 (quick pass)
M2 Monte Carlo (pred & control) W3
M3 TD for Prediction W3
M4 TD for Control (SARSA, Q-Learning) W5
M5 Planning, Learning & Acting (Dyna) W5
C3. Prediction & Control w/ Function Approximation
M1 Welcome W6 (quick pass)
M2 On-policy Prediction w/ Approx W6
M3 Constructing Features W6–W7
M4 Control w/ Approx W7
M5 Policy Gradient W14
C4. Capstone
M1-M6 Complete Capstone Project W9–W12

Hugging Face Course Coverage (100%)

Hugging Face Unit Week(s) Mapped Engineering Focus
Unit 0: Welcome to the Course W1 Environment setup, Docker containerization
Unit 1: Introduction to Deep RL (+ Huggy bonus) W2 Model deployment and versioning
Unit 2: Introduction to Q-Learning W3 Experiment tracking and reproducibility
Unit 3: Deep Q-Learning with Atari Games W6–W7 Model serving and API development
Unit 4: Policy Gradient with PyTorch W10 Advanced gradient methods and optimization
Unit 5: Introduction to Unity ML-Agents W12 Multi-environment deployment
Unit 6: Actor-Critic Methods with Robotics W15 Performance monitoring and scaling
Unit 7: Multi-Agent RL and AI vs AI W16 Distributed systems and coordination
Unit 8: Proximal Policy Optimization (PPO) W14 Advanced policy optimization and tuning

Portfolio Integration Points

  • Week 1: Professional Python package structure with pytest and type hints
  • Week 4: Automated testing pipeline with coverage reports and performance benchmarks
  • Week 8: Production-ready RL library with comprehensive test suite
  • Week 12: Deployed RL service with integration tests and monitoring
  • Week 16: Complete RL framework with advanced testing patterns and CI/CD

Key Deliverables: - Production-quality Python packages with >90% test coverage - Automated CI/CD pipelines with comprehensive testing - ~~RESTful API deployment with health checks and monitoring~~ - Performance benchmarking and statistical analysis frameworks - Advanced testing patterns (property-based, fuzzing, load testing)

rl_toolkit/
├── bandits/           # Week 1–2: k-armed bandits, ε-greedy, UCB
│   ├── epsilon_greedy.py
│   ├── ucb.py
│   └── thompson.py
├── mdp/               # Week 3–4: DP, value iteration, policy iteration
│   ├── value_iteration.py
│   └── policy_iteration.py
├── td/                # Week 5–6: MC, TD(0), SARSA, Q-learning
├── function_approx/   # Week 7–8: linear approx, NN
├── policy_gradient/   # Week 9–10: REINFORCE, Actor-Critic
├── advanced/          # Later: DQN, PPO, A3C
├── utils/
│   ├── seed.py        # reproducibility helpers
│   ├── plotting.py    # Matplotlib plots
│   └── torch_setup.py # device/AMP/determinism (we started this earlier)
└── examples/
    ├── bandits_demo.py
    └── value_iteration_demo.py

Outcomes at Week 16

Technical Portfolio: - Production-quality RL implementations with comprehensive test suites - Automated testing pipelines with performance benchmarking - RESTful API deployments with health monitoring and logging - Statistical analysis frameworks with automated reporting - Advanced testing methodologies (property-based, integration, load testing)

Skills Demonstrated: - Test-driven development for machine learning systems - Production-ready Python package development - Automated CI/CD for ML applications - Performance optimization and statistical analysis - API development and deployment with monitoring

Industry Readiness: - Portfolio showcasing professional software engineering practices - Comprehensive understanding of production ML system testing - Experience with modern Python development workflows - Preparation for enterprise RL system development in D.Eng. program

Resources

Reinforcement Learning

Engineering & Deployment

Applied Math

Industry Applications