def main():
filename_template = os.path.dirname(
os.path.realpath(__file__)
) + '/../../datasets/regression/{num_agents}_agents_{dataset_name}_cadrl_dataset_action_value_{mode}.p'
env, one_env = create_env()
modes = [
{
'mode': 'train',
'num_datapts': 100000,
},
{
'mode': 'test',
'num_datapts': 20000,
},
]
for mode in modes:
STATES, ACTIONS, VALUES = fill(env,
one_env,
num_datapts=mode['num_datapts'])
filename = filename_template.format(
mode=mode['mode'],
dataset_name=Config.DATASET_NAME,
num_agents=Config.MAX_NUM_AGENTS_IN_ENVIRONMENT)
file_dir = os.path.dirname(filename)
os.makedirs(file_dir, exist_ok=True)
with open(filename, "wb") as f:
pickle.dump([STATES, ACTIONS, VALUES], f)
print("Files written.")
def main():
env, one_env = create_env()
dt = one_env.dt_nominal
last_time = 0.0
plot_save_dir = file_dir + '/figs/'
os.makedirs(plot_save_dir, exist_ok=True)
pkl_dir = file_dir + '/trajs/'
os.makedirs(pkl_dir, exist_ok=True)
one_env.plot_save_dir = plot_save_dir
one_env.scenario = ["single_agents_swap", "single_agents_random_swap","single_agents_random_positions", "single_corridor_scenario"]
#one_env.scenario = ["agent_with_corridor"]
one_env.ego_policy = "RVOPolicy"
one_env.number_of_agents = 5
env.reset()
trajs = []
if not os.path.isfile(file_dir + '/trajs/'+ "dataset_with_images.csv"):
csvfile = open(file_dir + '/trajs/'+ "dataset.csv", 'w')
# Write header
writer = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
#writer.writerow(["Id","Time s","Time ns", "Position X",
# "Position Y", "Velocity X", "Velocity Y", "Goal X",
# "Goal Y", "coop_coef"])
test_case = 0
pbar = tqdm(total=num_test_cases)
id = 0
while test_case < num_test_cases:
times_to_goal, extra_times_to_goal, collision, all_at_goal, any_stuck, agents = run_episode(env, one_env)
# Change the global state history according with the number of steps required to finish the episode
#if all_at_goal:
for agent in agents:
agent.global_state_history = agent.global_state_history[:agent.step_num]
last_time = add_traj(agents, trajs, dt,last_time,writer)
test_case +=1
pbar.update(1)
if (test_case % 500 == 0) and (test_case>8):
fname = pkl_dir+'RVO'+ str(id) + '.pkl'
#fname = pkl_dir + 'RVO' + str(id) + '.json'
# Protocol 2 makes it compatible for Python 2 and 3
#json.dump(trajs, open(fname, 'a'),indent=test_case)
pickle.dump(trajs, open(fname,'wb'), protocol=2)
print('dumped {}'.format(fname))
trajs = []
id += 1
pbar.close()
print("Experiment over.")
def main():
env, one_env = create_env()
dt = one_env.dt_nominal
file_dir_template = os.path.dirname(os.path.realpath(
__file__)) + '/../results/{results_subdir}/{num_agents}_agents'
trajs = [[] for _ in range(num_test_cases)]
for num_agents in num_agents_to_test:
file_dir = file_dir_template.format(num_agents=num_agents,
results_subdir=results_subdir)
plot_save_dir = file_dir + '/figs/'
os.makedirs(plot_save_dir, exist_ok=True)
one_env.plot_save_dir = plot_save_dir
test_case_args['num_agents'] = num_agents
test_case_args['side_length'] = 7
for test_case in tqdm(range(num_test_cases)):
# test_case_args['test_case_index'] = test_case
# test_case_args['num_test_cases'] = num_test_cases
for policy in policies:
one_env.plot_policy_name = policy
policy_class = policies[policy]['policy']
test_case_args['agents_policy'] = policy_class
agents = test_case_fn(**test_case_args)
for agent in agents:
if 'checkpt_name' in policies[policy]:
agent.policy.env = env
agent.policy.initialize_network(**policies[policy])
one_env.set_agents(agents)
one_env.test_case_index = test_case
init_obs = env.reset()
times_to_goal, extra_times_to_goal, collision, all_at_goal, any_stuck, agents = run_episode(
env, one_env)
max_ts = [t / dt for t in times_to_goal]
trajs = add_traj(agents, trajs, dt, test_case, max_ts)
# print(trajs)
one_env.reset()
pkl_dir = file_dir + '/trajs/'
os.makedirs(pkl_dir, exist_ok=True)
fname = pkl_dir + policy + '.pkl'
pickle.dump(trajs, open(fname, 'wb'))
print('dumped {}'.format(fname))
print("Experiment over.")
def _set_env(self, id):
if Config.GAME_CHOICE == Config.game_grid:
self.game = Gridworld(id, Config.ENV_ROW, Config.ENV_COL,
Config.PIXEL_SIZE, Config.MAX_ITER,
Config.AGENT_COLOR, Config.TARGET_COLOR,
Config.DISPLAY_SCREEN, Config.TIMER_DURATION,
Config.IMAGE_WIDTH, Config.IMAGE_HEIGHT,
Config.STACKED_FRAMES, Config.DEBUG)
elif Config.GAME_CHOICE == Config.game_collision_avoidance:
from gym_collision_avoidance.experiments.src.env_utils import run_episode, create_env, store_stats
env, one_env = create_env()
self.game = env
else:
raise ValueError(
"[ ERROR ] Invalid choice of game. Check Config.py for choices"
)
def main():
np.random.seed(0)
test_case_fn = tc.formation
test_case_args = {}
env, one_env = create_env()
one_env.set_plot_save_dir(
os.path.dirname(os.path.realpath(__file__)) +
'/../results/cadrl_formations/')
for num_agents in Config.NUM_AGENTS_TO_TEST:
for policy in Config.POLICIES_TO_TEST:
np.random.seed(0)
prev_agents = None
for test_case in range(Config.NUM_TEST_CASES):
_ = reset_env(env, one_env, test_case_fn, test_case_args,
test_case, num_agents, policies, policy,
prev_agents)
episode_stats, prev_agents = run_episode(env, one_env)
return True
num_test_cases = 100
test_case_args = {}
Config.PLOT_CIRCLES_ALONG_TRAJ = True
vpref1 = False
radius_bounds = [0.5, 0.5]
if vpref1:
test_case_args['vpref_constraint'] = True
test_case_args['radius_bounds'] = radius_bounds
vpref1_str = 'vpref1.0_r{}-{}/'.format(radius_bounds[0], radius_bounds[1])
else:
vpref1_str = ''
Config.NUM_TEST_CASES = num_test_cases
env, one_env = create_env()
for num_agents in num_agents_to_test:
one_env.set_plot_save_dir(
os.path.dirname(os.path.realpath(__file__)) +
'/../results/full_test_suites/{vpref1_str}{num_agents}_agents/figs/'.
format(vpref1_str=vpref1_str, num_agents=num_agents))
test_case_args['num_agents'] = num_agents
stats = {}
for policy in policies:
stats[policy] = {}
stats[policy]['non_collision_inds'] = []
stats[policy]['all_at_goal_inds'] = []
stats[policy]['stuck_inds'] = []
def main():
np.random.seed(0)
test_case_fn = tc.full_test_suite
test_case_args = {}
if Config.FIXED_RADIUS_AND_VPREF:
radius_bounds = [0.5, 0.5]
test_case_args['vpref_constraint'] = True
test_case_args['radius_bounds'] = radius_bounds
vpref1_str = 'vpref1.0_r{}-{}/'.format(radius_bounds[0],
radius_bounds[1])
else:
vpref1_str = ''
env, one_env = create_env()
print(
"Running {test_cases} test cases for {num_agents} for policies: {policies}"
.format(
test_cases=Config.NUM_TEST_CASES,
num_agents=Config.NUM_AGENTS_TO_TEST,
policies=Config.POLICIES_TO_TEST,
))
with tqdm(total=len(Config.NUM_AGENTS_TO_TEST) *
len(Config.POLICIES_TO_TEST) * Config.NUM_TEST_CASES) as pbar:
for num_agents in Config.NUM_AGENTS_TO_TEST:
one_env.set_plot_save_dir(
os.path.dirname(os.path.realpath(__file__)) +
'/../results/full_test_suites/{vpref1_str}{num_agents}_agents/figs/'
.format(vpref1_str=vpref1_str, num_agents=num_agents))
for policy in Config.POLICIES_TO_TEST:
np.random.seed(0)
prev_agents = None
df = pd.DataFrame()
for test_case in range(Config.NUM_TEST_CASES):
##### Actually run the episode ##########
_ = reset_env(env, one_env, test_case_fn, test_case_args,
test_case, num_agents, policies, policy,
prev_agents)
episode_stats, prev_agents = run_episode(env, one_env)
df = store_stats(df, {
'test_case': test_case,
'policy_id': policy
}, episode_stats)
########################################
pbar.update(1)
if Config.RECORD_PICKLE_FILES:
file_dir = os.path.dirname(
os.path.realpath(__file__)
) + '/../results/full_test_suites/{vpref1_str}'.format(
vpref1_str=vpref1_str)
file_dir += '{num_agents}_agents/stats/'.format(
num_agents=num_agents)
os.makedirs(file_dir, exist_ok=True)
log_filename = file_dir + '/stats_{}.p'.format(policy)
# log_filename = file_dir+'/stats_{}_{}.p'.format(policy, now.strftime("%m_%d_%Y__%H_%M_%S"))
df.to_pickle(log_filename)
return True