def save_video(genome, agent, env, num_steps, file_name='evo_run.mp4'):
if genome is not None:
agent.set_weights(genome)
video_path = '/' + file_name
helper.make_video_plark_env(agent, env, video_path, n_steps=num_steps)
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)
panther_dummy_agent = PantherNN(
num_inputs=num_inputs,
num_hidden_layers=num_hidden_layers,
neurons_per_hidden_layer=neurons_per_hidden_layer)
#pelican_dummy_agent = PelicanNN(file_dir_name='pelican_20210302_195211', game=game,
# driving_agent=True)
#Set agent
#game.pelicanAgent = pelican_dummy_agent
dummy_env.reset()
'''
max_num_steps = 1
reward = 0
obs = dummy_env._observation()
for step_num in range(max_num_steps):
action = panther_dummy_agent.getAction(obs)
obs, r, done, info = dummy_env.step(action)
reward += r
if done:
break
'''
video_path = '/load_evo_non_driving_new.mp4'
helper.make_video_plark_env(panther_dummy_agent,
dummy_env,
video_path,
n_steps=1)
# num_hidden_layers=num_hidden_layers,
# neurons_per_hidden_layer=neurons_per_hidden_layer)
pelican_dummy_agent = PelicanNN(
file_dir_name='pelican_20210309_100850_gen_2',
game=game,
driving_agent=True)
#Set agent
#game.pelicanAgent = pelican_dummy_agent
dummy_env.reset()
'''
max_num_steps = 1
reward = 0
obs = dummy_env._observation()
for step_num in range(max_num_steps):
action = panther_dummy_agent.getAction(obs)
obs, r, done, info = dummy_env.step(action)
reward += r
if done:
break
'''
video_path = '/load_evo_non_driving_new.mp4'
helper.make_video_plark_env(pelican_dummy_agent,
dummy_env,
video_path,
n_steps=200)