Compare RL Algorithms → Generate Research Report → Share Findings

Execute multiple DQN variants (DQN, Double DQN, Dueling DQN, Prioritized Experience Replay) on standardized environments like Atari games. Use the same hyperparameters and training duration for fair comparison.

Log all training runs to W&B with detailed metrics including sample efficiency, final performance, training stability, and computational requirements. Tag experiments by algorithm variant and environment for easy filtering.

Create analysis notebooks that pull data from W&B API to generate comparison plots, statistical significance tests, and performance rankings. Include convergence speed analysis and sample efficiency metrics.

Feed the analysis results to ChatGPT with a prompt template to generate structured research findings, including methodology description, key results, and recommendations for algorithm selection based on different use cases.

Import the generated report into a Notion database with tags for algorithms, environments, and performance metrics. Create templates for future experiments and link to relevant papers and code repositories.