Learn how to build a complete reinforcement learning framework from scratch in C. The course begins with constructing a custom computational graph and automatic differentiation (autograd) engine to handle matrix operations alongside forward and backward passes. It then walks through developing a standalone Snake game environment from the ground up, including custom state vector encoding and collision reward logic. Finally, the lesson ties everything together by implementing the REINFORCE policy gradient algorithm, trajectory rollouts, and an end-to-end training pipeline to train the agent.
? Code: https://github.com/harshbhatt7585/cRL
✏️ Course created by @harshbhatt7585
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⭐️ Chapters ⭐️
- 0:00:00 Introduction & Overview
- 0:02:48 Setting Up the Autograd Engine & Variables
- 0:16:09 Matrix Creation & Allocation Logic
- 0:26:27 Building Nodes & Computational Graph Traversal
- 0:56:12 Forward & Backward Activation Operations
- 1:04:40 Matrix Multiplication (Row-Major & Transpose Operations)
- 1:23:49 Building the Snake Game RL Environment
- 1:37:16 State Vector Encoding & Reward System
- 1:44:10 Training Pipeline & Rollout Buffers Setup
- 1:59:22 Implementing the Actor-Critic Model
- 2:07:39 Computing Returns, Advantages & Backward Pass
- 2:14:41 Optimizer Step, Evaluation & Conclusion
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