Learning path modules (interactive hubs) on Reinforcement Learning Curriculum

Learning path modules (interactive hubs) on Reinforcement Learning Curriculum https://codefrydev.in/Reinforcement/learning-path/modules/ Recent content in Learning path modules (interactive hubs) on Reinforcement Learning Curriculum Reinforcement Learning Curriculum https://codefrydev.in/Reinforcement/og-default.png https://codefrydev.in/Reinforcement/og-default.png Hugo en-us Mon, 01 Jan 0001 00:00:00 +0000 Phase 0 — Programming from zero https://codefrydev.in/Reinforcement/learning-path/modules/phase-0/ Mon, 01 Jan 0001 00:00:00 +0000 https://codefrydev.in/Reinforcement/learning-path/modules/phase-0/ Install Python, run your first script, and learn variables, conditionals, loops, and functions before RL. Phase 1 — Math foundations for RL https://codefrydev.in/Reinforcement/learning-path/modules/phase-1/ Mon, 01 Jan 0001 00:00:00 +0000 https://codefrydev.in/Reinforcement/learning-path/modules/phase-1/ Probability, statistics, linear algebra, and calculus with RL-motivated examples. Read in order: 1a → 1d → self-check. Phase 2 — Prerequisites (tools and libraries) https://codefrydev.in/Reinforcement/learning-path/modules/phase-2/ Mon, 01 Jan 0001 00:00:00 +0000 https://codefrydev.in/Reinforcement/learning-path/modules/phase-2/ Python, NumPy, PyTorch, Gym/Gymnasium, and related tools the curriculum assumes. Complete tasks on the prerequisites index, then the Phase 2 quiz. Phase 3 — Math for RL (deep dive) https://codefrydev.in/Reinforcement/learning-path/modules/phase-3/ Mon, 01 Jan 0001 00:00:00 +0000 https://codefrydev.in/Reinforcement/learning-path/modules/phase-3/ Deeper pass through the same math areas as Phase 1, with more drills and RL-motivated examples before you start the core RL volumes. Phase 4 — ML foundations https://codefrydev.in/Reinforcement/learning-path/modules/phase-4/ Mon, 01 Jan 0001 00:00:00 +0000 https://codefrydev.in/Reinforcement/learning-path/modules/phase-4/ Supervised learning, regression, classification, gradient descent, and evaluation—before neural networks for RL. Phase 5 — DL foundations https://codefrydev.in/Reinforcement/learning-path/modules/phase-5/ Mon, 01 Jan 0001 00:00:00 +0000 https://codefrydev.in/Reinforcement/learning-path/modules/phase-5/ Neural networks, backpropagation, CNNs, PyTorch patterns, and a mini-project—directly reusable for DQN, policies, and actor-critic. Phase 6 — RL foundations (tabular) https://codefrydev.in/Reinforcement/learning-path/modules/phase-6/ Mon, 01 Jan 0001 00:00:00 +0000 https://codefrydev.in/Reinforcement/learning-path/modules/phase-6/ Volumes 1–2: MDPs, dynamic programming, Monte Carlo, TD, SARSA, and tabular Q-learning. Core theory before function approximation. Phase 7 — Deep RL https://codefrydev.in/Reinforcement/learning-path/modules/phase-7/ Mon, 01 Jan 0001 00:00:00 +0000 https://codefrydev.in/Reinforcement/learning-path/modules/phase-7/ Volumes 3–5: value function approximation, DQN family, policy gradients, actor-critic, and advanced policy optimization (chapters 21–50). Phase 8 — Advanced topics https://codefrydev.in/Reinforcement/learning-path/modules/phase-8/ Mon, 01 Jan 0001 00:00:00 +0000 https://codefrydev.in/Reinforcement/learning-path/modules/phase-8/ Volumes 6–10: model-based RL, exploration, offline RL, MARL, real-world RL, safety, and RL with LLMs (chapters 51–100).