Welcome to my personal repository for learning and exploring Deep Reinforcement Learning (Deep RL)! This repository documents my journey as I work through a course curriculum based on resources from Spinning Up in Deep RL and Berkeley's Deep RL Course. All code, notes, and projects here are for my own study, experimentation, and research exploration.
- Course Overview
- Course Outline & Weekly Breakdown
- Practice Projects & Homework Assignments
- Recommended Research Papers
- Additional Resources & Tips
- How I Use This Repository
- License
This 16‑week course provides an in‐depth exploration of reinforcement learning with an emphasis on deep learning methods. I will start with the basics of Markov Decision Processes (MDPs) and progress to advanced topics such as actor–critic methods, trust region methods, multi-agent RL, and more. The course includes both theoretical lectures and hands‑on programming assignments/projects.
- Mathematics: Linear algebra, probability, and calculus.
- Programming: Python (experience with libraries such as NumPy, PyTorch/TensorFlow is beneficial).
- Machine Learning: Basic understanding of supervised learning and neural networks.
- Topics:
- What is reinforcement learning?
- Historical context and motivations.
- Overview of deep RL applications (robotics, games, finance, etc.).
- Readings & Videos:
- Spinning Up “Introduction” section.
- Berkeley course introduction lecture (if available).
- Homework:
- Short Essay: Write a 500–700 word review on a real-world RL application, discussing its impact and challenges.
- Topics:
- MDP components (states, actions, rewards, transitions).
- Policies, returns, and value functions.
- The Bellman equation and optimality.
- Readings:
- Relevant chapters in Spinning Up.
- Berkeley lecture notes on MDPs.
- Homework:
- Exercise Set: Solve problems on computing returns and performing policy evaluation on simple MDPs (e.g., a gridworld).
- Topics:
- Dynamic Programming techniques for RL.
- Monte Carlo policy evaluation and control.
- Limitations and convergence properties.
- Readings:
- Spinning Up sections on Monte Carlo methods.
- Berkeley course material on DP/MC.
- Homework:
- Programming Assignment: Implement a Monte Carlo policy evaluation algorithm in Python on a small environment (e.g., Blackjack or gridworld).
- Topics:
- TD methods and the TD error.
- Q-Learning vs. SARSA.
- Convergence issues and exploration strategies.
- Readings:
- Spinning Up sections on TD learning.
- Berkeley lecture notes on Q-Learning.
- Homework:
- Coding Task: Implement Q-learning on a gridworld and visualize the Q‑values as they converge over episodes.
- Topics:
- The challenge of high-dimensional state spaces.
- Neural network function approximators.
- Experience replay, target networks, and stability tricks in DQN.
- Readings:
- Spinning Up’s DQN tutorial.
- Berkeley course lecture on deep Q‑learning.
- Homework:
- Programming Project: Implement a DQN to solve the CartPole-v1 environment. Experiment with replay buffer sizes and target update frequencies.
- Topics:
- Derivation of the policy gradient theorem.
- The REINFORCE algorithm.
- Variance reduction techniques.
- Readings:
- Spinning Up’s policy gradient sections.
- Berkeley lecture material on policy gradients.
- Homework:
- Coding Exercise: Implement the REINFORCE algorithm on a simple environment (e.g., MountainCar or Pendulum).
- Topics:
- Combining value-based and policy-based approaches.
- Actor–critic architecture and the advantage function.
- Baseline subtraction and variance reduction.
- Readings:
- Spinning Up sections on Actor-Critic methods.
- Berkeley lecture on actor–critic algorithms.
- Homework:
- Implementation Task: Develop an actor–critic algorithm and test it on a medium-difficulty task. Compare its performance with pure policy gradient methods.
- Topics:
- Motivation for trust region methods.
- Detailed exploration of TRPO.
- Simplifications leading to PPO.
- Readings:
- Original TRPO paper by Schulman et al. (2015).
- PPO paper by Schulman et al. (2017).
- Spinning Up’s summaries.
- Homework:
- Comparative Study: Implement (or utilize an existing implementation of) PPO on an OpenAI Gym environment (e.g., LunarLander) and compare its learning curve to that of TRPO if feasible.
- Topics:
- The exploration–exploitation trade-off.
- Epsilon-greedy, Boltzmann exploration, and entropy regularization.
- Reward shaping and intrinsic motivation.
- Readings:
- Spinning Up’s discussion on exploration techniques.
- Selected Berkeley slides or supplementary materials.
- Homework:
- Experiment: Modify an existing algorithm (e.g., DQN or PPO) to incorporate an advanced exploration strategy and analyze its impact.
- Topics:
- Differences between model-free and model-based approaches.
- Learning and utilizing a model of the environment.
- The Dyna framework and planning methods.
- Readings:
- Spinning Up’s sections on model-based RL.
- Berkeley course readings on planning and model-based techniques.
- Homework:
- Project: Implement a simple model-based RL algorithm (using a Dyna-style architecture) on a simulated environment.
- Topics:
- Cooperative vs. competitive multi-agent settings.
- Centralized training with decentralized execution.
- Challenges such as non-stationarity and scalability.
- Readings:
- Research papers such as "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments" (Lowe et al.).
- Berkeley lecture notes on multi-agent systems (if available).
- Homework:
- Simulation: Create a basic multi-agent environment (e.g., a cooperative gridworld) and implement independent learning agents. Analyze coordination dynamics.
- Topics:
- Hierarchical reinforcement learning: Options framework and subgoal discovery.
- Meta-learning: How agents can “learn to learn.”
- Applications and challenges in hierarchical RL.
- Readings:
- Review articles and research papers on hierarchical RL and meta-RL.
- Spinning Up and Berkeley advanced topic materials.
- Homework:
- Paper Review & Presentation: Select a research paper on hierarchical or meta-RL, prepare a 10–15 minute presentation, and lead a discussion.
- Topics:
- Safe exploration and risk-sensitive learning.
- Robustness against adversarial environments.
- Ethical considerations and fairness in decision making.
- Readings:
- Recent survey papers on safe RL and robust RL strategies.
- Selected Berkeley materials or external resources.
- Homework:
- Case Study Analysis: Analyze a real-world scenario where safety and robustness are critical, and write a report discussing current approaches and potential improvements.
- Topics:
- Applications in robotics, autonomous driving, games, finance, healthcare, etc.
- Industry case studies.
- Integration of RL with other learning paradigms.
- Readings:
- Spinning Up applied sections and selected research articles/case studies.
- Berkeley guest lectures or seminars (if available).
- Homework:
- Concept Proposal: Choose an application area and outline an RL-based solution, discussing algorithm choices, challenges, and evaluation metrics.
- Topics:
- Survey of current research frontiers in deep RL.
- Open problems: sample efficiency, generalization, transfer learning.
- Reproducibility and benchmarking.
- Readings:
- Survey articles such as Deep Reinforcement Learning: An Overview.
- Recent arXiv preprints and workshop papers.
- Homework:
- Research Proposal: Write a 2–3 page research proposal identifying an open problem in deep RL and outlining a methodology to address it.
- Topics:
- Presentation of final projects.
- Peer review and discussion.
- Course summary and future directions.
- Final Project:
- Comprehensive Project: Develop a final project that could include a complete RL system, an extension of an existing algorithm, or a research study exploring a novel idea.
- Presentation: Prepare a 15–20 minute presentation summarizing your work.
-
Simple MDP Exercises:
- Compute returns and evaluate policies in small gridworlds.
-
Monte Carlo & TD Learning Implementations:
- Code Monte Carlo and Q-learning algorithms on toy environments.
-
Deep Q-Network (DQN):
- Develop a DQN to solve classic control problems (e.g., CartPole).
-
Policy Gradient & Actor-Critic Methods:
- Implement REINFORCE and actor–critic algorithms; compare their performance.
-
Advanced Algorithms (TRPO/PPO):
- Experiment with PPO (and optionally TRPO) on environments such as LunarLander.
-
Exploration & Model-Based RL Experiments:
- Test advanced exploration strategies and implement a simple model-based RL algorithm.
-
Multi-Agent Simulations:
- Build a multi-agent environment and study coordination dynamics.
-
Final Research-Oriented Project:
- Propose, implement, and analyze an original RL project or algorithm extension.
-
Foundational & Breakthrough Papers:
- Playing Atari with Deep Reinforcement Learning – Mnih et al. (2013)
- Human-level Control through Deep Reinforcement Learning – Mnih et al. (2015)
-
Advanced Methods & Improvements:
- Asynchronous Methods for Deep Reinforcement Learning – Mnih et al. (2016)
- Trust Region Policy Optimization – Schulman et al. (2015)
- Proximal Policy Optimization Algorithms – Schulman et al. (2017)
- Rainbow: Combining Improvements in Deep Reinforcement Learning – Hessel et al. (2018)
-
Specialized Topics:
- Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments – Lowe et al. (2017)
- Curiosity-driven Exploration by Self-supervised Prediction – Pathak et al. (2017)
- Survey papers such as Deep Reinforcement Learning: An Overview – Li (2017) (or similar)
-
Recent Trends:
- Explore recent survey articles and workshop proceedings on sample efficiency, meta-learning in RL, and safe exploration.
-
Programming Environment:
Use OpenAI Gym, PyTorch or TensorFlow, and frameworks like Stable Baselines or RLlib. -
Reproducibility:
Keep your code well-documented, use version control (e.g., Git), and maintain clear experiment logs. -
Community Engagement:
Join forums, discussion groups, or reading clubs (e.g., the OpenAI Spinning Up community) to exchange ideas and solve challenges. -
Iterative Learning:
Start with simple environments and progressively tackle more challenging tasks as you refine your algorithms.
This repository is exclusively for my personal learning and exploration in deep reinforcement learning. I document my study notes, code experiments, and project progress here. While others might find the materials useful, please note that the content is tailored to my learning journey and may evolve over time as I experiment and learn more.
The repository is organized by week and topic, so I can easily revisit concepts, review my code, and track my progress.
Example directory structure: