def evaluate(genome, config_file_path, driving_agent, normalise_obs,
domain_params_in_obs, num_trials):
#Instantiate the env
env = PlarkEnvSparse(config_file_path=config_file_path,
image_based=False,
driving_agent=driving_agent,
normalise=normalise_obs,
domain_params_in_obs=domain_params_in_obs)
num_inputs = len(env._observation())
num_hidden_layers = 0
neurons_per_hidden_layer = 0
if trained_agent == 'panther':
agent = PantherNN(num_inputs=num_inputs,
num_hidden_layers=num_hidden_layers,
neurons_per_hidden_layer=neurons_per_hidden_layer)
else:
agent = PelicanNN(num_inputs=num_inputs,
num_hidden_layers=num_hidden_layers,
neurons_per_hidden_layer=neurons_per_hidden_layer)
agent.set_weights(genome)
reward = 0
for i in range(num_trials):
env.reset()
obs = env._observation()
trial_reward = 0
while True:
action = agent.getAction(obs)
obs, r, done, info = env.step(action)
trial_reward += r
if done:
break
reward += trial_reward
#Average trial reward
reward /= num_trials
#agent.save_agent(obs_normalise=normalise_obs, domain_params_in_obs=domain_params_in_obs)
#print("Finished at step num:", step_num)
#print("Reward:", reward)
#print("Status:", info['status'])
#save_video(genome, agent, env, max_num_steps, file_name='evo.mp4')
#exit()
return [reward]
if __name__ == '__main__':
#Env variables
config_file_path = '/Components/plark-game/plark_game/game_config/10x10/nn/nn_coevolution_balanced.json'
normalise_obs = True
#Instantiate dummy env and dummy agent
#I need to do this to ascertain the number of weights needed in the optimisation
#procedure
dummy_env = PlarkEnvSparse(config_file_path=config_file_path,
image_based=False,
driving_agent='panther',
normalise=normalise_obs)
#Neural net variables
num_inputs = len(dummy_env._observation())
num_hidden_layers = 0
neurons_per_hidden_layer = 0
panther_dummy_agent = PantherNN(
num_inputs=num_inputs,
num_hidden_layers=num_hidden_layers,
neurons_per_hidden_layer=neurons_per_hidden_layer)
#I need to figure out how to get rid of the 139 magic number
pelican_dummy_agent = PelicanNN(
num_inputs=139,
num_hidden_layers=num_hidden_layers,
neurons_per_hidden_layer=neurons_per_hidden_layer)
#num_panther_weights = panther_dummy_agent.get_num_weights()
#num_pelican_weights = pelican_dummy_agent.get_num_weights()
