yawning_titan.agents.nsa_red doesnt seem to be working (TypeError: 'NSARed' object is not callable)

[RiccardoGTolli]

When I try to create a NSARed agent it goes without errors but when I try to use it in yawning_titan.yawning_titan_run.YawningTitanRun it returns this error : TypeError: 'NSARed' object is not callable

Is there any other way to use NSARed?

# Provides the red agent behaviour within the `18-node-def` environment.
from yawning_titan.agents.nsa_red import NSARed

from stable_baselines3.common.env_checker import check_env
from stable_baselines3.common.evaluation import evaluate_policy


from yawning_titan.envs.generic.core.blue_interface import BlueInterface
from yawning_titan.envs.generic.core.red_interface import RedInterface
from yawning_titan.envs.generic.generic_env import GenericNetworkEnv
from yawning_titan.envs.generic.core.action_loops import ActionLoop
from yawning_titan.envs.generic.core.network_interface import NetworkInterface
from yawning_titan.networks.network_db import default_18_node_network
from yawning_titan.game_modes.game_mode_db import default_game_mode


# Build the Network
network = default_18_node_network()
# Build the GameModeConfig
game_mode = default_game_mode()
# Build the NetworkInterface
network_interface = NetworkInterface(game_mode=game_mode, network=network)

# Create the red and blue agents
red = RedInterface(network_interface)
blue = BlueInterface(network_interface)

# Set the red agent to the NSARed premade
red=NSARed(skill=0.5,
       action_set=red.action_set,
       action_probabilities=red.action_probabilities,
       node_set=red.node_set,
       zd_start_amount=0,
       zd_gain=0,
       zd_required=10)

# Create the environment
env = GenericNetworkEnv(red, blue, network_interface)
# check it s gym compliant;its not 
check_env(env, warn=True)
# reset any change during the check 
# _ = env.reset()

from yawning_titan.yawning_titan_run import YawningTitanRun
YawningTitanRun(network=default_18_node_network(),
                game_mode=default_game_mode(),
                red_agent_class=red,
                blue_agent_class=blue,
                print_metrics=False,
                show_metrics_every=1,
                collect_additional_per_ts_data=False,
                eval_freq=10000,
                total_timesteps=200000,
                training_runs=1,
                n_eval_episodes=1,
                deterministic=False,
                warn=True,
                render=False,
                verbose=1,
                logger=None,
                output_dir=None,
                auto=True)

[ChrisMcCarthyDev]

Thank you for raising this. This has been added to our internal backlog (reference 1645) and will be looked into by one of the dev team soon.