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]
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()
#Let's try instantiating with dummy agents and setting the agents competing against each
#other
dummy_env.reset()
max_num_steps = 200
reward = 0
obs = dummy_env._observation()
for step_num in range(max_num_steps):
action = panther_dummy_agent.getAction(obs)
print(action)
obs, r, done, info = dummy_env.step(action)
reward += r
if done:
break
driving_agent = 'pelican'
random_panther_start_position = True
random_pelican_start_position = True
env = PlarkEnvSparse(
config_file_path=config_file_path,
driving_agent=driving_agent,
random_panther_start_position=random_panther_start_position,
random_pelican_start_position=random_pelican_start_position)
#This is the only difference to a normal environment - one has to set the game
#to a RuleBasedGame
env.env.activeGames[len(env.env.activeGames) -
1] = create_rule_based_game(config_file_path)
env.reset()
reward = 0
while True:
_, r, done, info = env.step(None)
reward += r
if done:
break
print(info['status'])
print("Reward:", reward)
video_path = '/rule_v_rule.mp4'
helper.make_video_plark_env(None, env, video_path, n_steps=200)
