snazau / jbr-rl-mountaincar-v0 Goto Github PK

A solution for mountain car problem via Q-learning with algorithmic and deep approaches.

• Requirements
• How to run
• DQL results
• QL results

• PyTorch (instructions)
• NumPy (instructions)
• gym (instructions)
• seaborn (instructions)

• Clone this repo to your local computer
• Install all required dependencies
• ???
• Type in console:
• • python deep_Q_learning.py if you want to run deep-approach version
• • python Q_learning.py if you want to run algorithmic-approach version
• You're good now

Results with deep approach with following parameters:

• step_amount = 100001
• learning_rate = 0.001
• discount_rate = 0.99
• hidden_size_1 = 64
• hidden_size_2 = 64
• max_eps = 0.5
• min_eps = 0.1
• batch_size = 256
• sync_models = 1000
• best_step = 69000
• activation = prelu

Avg reward: Policy, 0 - move left, 1 - do nothing, 2 - move right: Corresponding values:

Results with algorithmic approach with following parameters:

• episode_amount = 40001
• learning_rate = 0.2
• discount_rate = 0.9
• max_eps = 0.5
• min_eps = 0.1
• grid_size = 80 * 60

Avg reward: Policy, 0 - move left, 1 - do nothing, 2 - move right: Corresponding values: