Setup RL Environment → Train Agent → Create Demo Video

Define a custom Gym environment that represents your specific problem domain (trading, robotics, optimization). Implement the observation space, action space, reward function, and environment dynamics following Gym's interface standards.

Use the DQN implementation from Baselines to train an agent on your custom environment. Start with default hyperparameters and adjust based on initial results. Save checkpoints at regular intervals to capture learning progress.

Create a script that loads the trained model and records the agent's performance in the environment. Use OpenCV to capture frames and compile them into a video showing the agent's decision-making process and improvement over time.

Design professional slides explaining the problem, solution approach, and results. Include before/after performance graphs, key metrics, and embedded video clips of the agent in action. Use Canva's templates for technical presentations.