Sorry for the weird phrasing on the title, had to keep it short.
I want to change the max_step parameter in the configurations, and despite the Behavior Name in Unity matching the one specified in configuration.yaml (that name being “WarshAgent”), MLAgents still insists on hitting me with this:
[WARNING] Behavior name WarshAgent does not match any behaviors specified in the trainer configuration file. A default configuration will be used.
And then uses a default configuration, so everytime I changed the max_step, it would get changed back to the default value.
I did try using the command "python -m mlagents.trainers.upgrade_config configuration.yaml
WarshAgent.yaml" but all that did was copy the content of configuration.yaml to WarshAgent.yaml, even when I deleted configuration.yaml it still created a new one and completely ignored WarshAgent.yaml.
Here are more details:
Behavior Parameters:
configuration.yaml:
default_settings: null
behaviors:
WarshAgent:
trainer_type: ppo
hyperparameters:
batch_size: 1024
buffer_size: 10240
learning_rate: 0.0003
beta: 0.005
epsilon: 0.2
lambd: 0.95
num_epoch: 3
learning_rate_schedule: linear
beta_schedule: linear
epsilon_schedule: linear
network_settings:
normalize: false
hidden_units: 128
num_layers: 2
vis_encode_type: simple
memory: null
goal_conditioning_type: hyper
deterministic: false
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
network_settings:
normalize: false
hidden_units: 128
num_layers: 2
vis_encode_type: simple
memory: null
goal_conditioning_type: hyper
deterministic: false
init_path: null
keep_checkpoints: 5
checkpoint_interval: 500000
max_steps: 500000
time_horizon: 64
summary_freq: 50000
threaded: false
self_play: null
behavioral_cloning: null
env_settings:
env_path: null
env_args: null
base_port: 5005
num_envs: 1
num_areas: 1
seed: -1
max_lifetime_restarts: 10
restarts_rate_limit_n: 1
restarts_rate_limit_period_s: 60
engine_settings:
width: 84
height: 84
quality_level: 5
time_scale: 20
target_frame_rate: -1
capture_frame_rate: 60
no_graphics: false
environment_parameters: null
checkpoint_settings:
run_id: ppo
initialize_from: null
load_model: false
resume: true
force: false
train_model: false
inference: false
results_dir: results
torch_settings:
device: null
debug: false
WarshAgent.yaml:
behaviors:
WarshAgent:
trainer_type: ppo
hyperparameters:
batch_size: 1024
buffer_size: 10240
learning_rate: 0.0003
beta: 0.005
epsilon: 0.2
lambd: 0.95
num_epoch: 3
learning_rate_schedule: linear
beta_schedule: linear
epsilon_schedule: linear
network_settings:
normalize: false
hidden_units: 128
num_layers: 2
vis_encode_type: simple
goal_conditioning_type: hyper
deterministic: false
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
network_settings:
normalize: false
hidden_units: 128
num_layers: 2
vis_encode_type: simple
goal_conditioning_type: hyper
deterministic: false
keep_checkpoints: 5
checkpoint_interval: 500000
max_steps: 900000000
time_horizon: 64
summary_freq: 50000
threaded: false
env_settings:
base_port: 5005
num_envs: 1
num_areas: 1
seed: -1
max_lifetime_restarts: 10
restarts_rate_limit_n: 1
restarts_rate_limit_period_s: 60
engine_settings:
width: 84
height: 84
quality_level: 5
time_scale: 20
target_frame_rate: -1
capture_frame_rate: 60
no_graphics: false
checkpoint_settings:
run_id: ppo
load_model: false
resume: true
force: false
train_model: false
inference: false
results_dir: results
torch_settings: {}
debug: false
What am I doing wrong? Or am is my understanding of how this works is just wrong?