Located in config/trainer_config.yaml
This is the configuration about Reinforcement Learning PPO trainer
AutoBenchBrain:
batch_size: 1024
beta: 1.0e-1
buffer_size: 1024
epsilon: 0.2
gamma: 0.99
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 1.0e7
memory_size: 256
normalize: true
num_epoch: 5
num_layers: 2
time_horizon: 512
sequence_length: 64
summary_freq: 3000
use_recurrent: true
Located in config/curricula/autobench/AutoBenchBrain.json This is about the configuration of Unity environment
{
"measure": "progress", #Ignore
"thresholds": [], #Ignore
"min_lesson_length": 0, #Ignore
"signal_smoothing": false, #Ignore
"parameters": {
"camera1_type": [0],
"camera2_type": [3],
"camera3_type": [0],
"camera1_res_x": [0],
"camera2_res_x": [50],
"camera3_res_x": [0],
"camera1_res_y": [0],
"camera2_res_y": [50],
"camera3_res_y": [0],
"weather_id": [1],
"time_id": [9],
"road_width": [7],
"forward": [true],
"detail": [false],
"goal_reward": [500],
"time_penalty": [-1],
"collision_penalty": [-300],
"position_reward": [300],
"velocity_reward": [1]
}
}
Only need to focus on parameters
section
Located in learn_rl.py, learn_ml.py, learn_gym.py
The following uses learn_rl.py as an example
env_path = 'AutoBenchExecutable/AutoBenchExecutable' #Default executable path
run_id = '1'
load_model = False
train_model = True
save_freq = 10000
keep_checkpoints = 1000
worker_id = 0
run_seed = 0
curriculum_folder = 'config/curricula/autobench/'
curriculum_file = 'config/curricula/autobench/AutoBenchBrain.json'
lesson = 0
fast_simulation = True
no_graphics = False
trainer_config_path = 'config/trainer_config.yaml'
benchmark = False
benchmark_episode = 100
benchmark_verbose = True
Path of the Unity executable
Identifier for each run, suitable for fine-tunning parameters
Whether load the tensorflow model
Whether train the tensorflow model
Frequency of the tensorflow model saved
Maximum checkpoint allow for saving
Ignore and set to 0
Random seed of the Unity executable
Folder of environment config file
Location of environment config file
Ignore and set to 0
If set to True, small window, 100X time scale, 10 agents
If set to False, large window, 1X time scale, 1 agent and WASD-controled Observe Camera
Whether not showing the windows of Unity environment
Location of trainer config file
Whether benchmark the current model
Number of episode needed for benchmarking
Whether or not print out episode information if episode ends