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- Load pendulum gymnasium environment
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Classroom Contents
Hyperparameter Optimization for Reinforcement Learning Using Meta's Ax
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- 1 - Introduction
- 2 - What are hyperparameters
- 3 - Hyperparameter optimization loop
- 4 - Grid search
- 5 - Random search
- 6 - Bayesian optimization
- 7 - Install Python packages
- 8 - Import Python packages
- 9 - Configure Weights & Biases
- 10 - Set deterministic mode
- 11 - Load pendulum gymnasium environment
- 12 - Test pendulum environment
- 13 - Test random actions with dummy agent
- 14 - Testing and logging callbacks
- 15 - Define trial to train and test an agent
- 16 - Define project settings and hyperparameter ranges
- 17 - Create gymnasium environment
- 18 - Define Ax experiment to perform Bayesian optimization for hyperparameters
- 19 - Perform hyperparameter optimization and debugging
- 20 - Train agent with best hyperparameters
- 21 - Test agent
- 22 - Run additional trials
- 23 - Weights & Biases sweeps
