#args['out_length'] = 25
#args['grid_size'] = (13,3)
#args['soc_conv_depth'] = 64
#args['conv_3x1_depth'] = 16
#args['dyn_embedding_size'] = 32
#args['input_embedding_size'] = 32
#args['num_lat_classes'] = 3
#args['num_lon_classes'] = 2
#args['use_maneuvers'] = True
args = {}
args['train_flag'] = True
args['model_dir'] = 'experiments/' + cmd_args.experiment
args['restore_file'] = cmd_args.restore_file # or 'last' or 'best'
utils.set_logger(os.path.join(args['model_dir'], 'train.log'))
json_path = os.path.join(args['model_dir'], 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = utils.Params(json_path)
params.grid_size = (params.grid_size_lon, params.grid_size_lat)
# use GPU if available
params.use_cuda = torch.cuda.is_available()
params.train_flag = args['train_flag']
params.model_dir = args['model_dir']
print("\nEXPERIMENT:", args['model_dir'], "\n")
if "transformer" in cmd_args.experiment:
#args['in_length'] = 16
#args['out_length'] = 25
#args['grid_size'] = (13,3)
#args['soc_conv_depth'] = 64
#args['conv_3x1_depth'] = 16
#args['dyn_embedding_size'] = 32
#args['input_embedding_size'] = 32
#args['num_lat_classes'] = 3
#args['num_lon_classes'] = 2
#args['use_maneuvers'] = True
args = {}
args['train_flag'] = False
args['model_dir'] = 'experiments/' + cmd_args.experiment
utils.set_logger(os.path.join(args['model_dir'], 'evaluate.log'))
json_path = os.path.join(args['model_dir'], 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = utils.Params(json_path)
params.grid_size = (params.grid_size_lon, params.grid_size_lat)
# use GPU if available
params.use_cuda = torch.cuda.is_available()
#params.use_cuda = False
params.train_flag = args['train_flag']
params.model_dir = args['model_dir']
params.create_onnx = True
"{:.6f} sec, hardbrakes: {:.2f}, steps_to_goal: {:.2f}, "
"steps_to_collision {:.2f}, "
"speed_at_collision {:.2f}".format(np.mean(metric_scores),
success / len(metric_scores),
np.mean(metric_runtime),
np.mean(metric_hardbrakes),
np.mean(metric_steps_to_goal),
np.mean(metric_steps_to_collision),
np.mean(metric_speed_at_collision)))
plot(metric_steps_to_goal, metric_steps_to_collision, args.algo)
## MAIN starts here ###
utils.set_logger('logs/test.log')
# Parser
# python3 test_algo.py
# python3 test_algo.py --algo dqn
parser = argparse.ArgumentParser()
parser.add_argument(
'--algo',
default='baseline',
help="baseline, qlearning, dqn, mcts, mcts-nnet, mpc, human")
parser.add_argument('--nn', default='dnn', help="dnn or cnn")
parser.add_argument('--restore',
default=None,
help="Optional, file in models containing weights to load")
parser.add_argument('--visu',
default=False,
help="Optional, used to debug and visualize tests")
# done = mdp.isEnd(sp)[0]
# #agent.step(s, a, r, sp, done)
# memory.add(s, a, r, sp, done)
# if len(memory) > BATCH_SIZE:
# samples = memory.sample()
# for sample in samples:
# state, action, reward, next_state, isDone = sample
# rl.incorporateFeedback(state, action, reward, next_state, isDone)
# else:
# rl.incorporateFeedback(s, a, r, sp, done)
# score += r
# if done:
# break
# s = sp
# scores_window.append(score)
# eps = max(eps_end, eps_decay*eps)
# avg_sliding_score = np.mean(scores_window)
# print("Episode {} Average sliding score: {:.2f}".format(i_episode, avg_sliding_score))
# if avg_sliding_score > -10:
# weightsFile.write("Episode {} Average sliding score: {:.2f}\n".format(i_episode, avg_sliding_score))
# rl.dumpWeights()
utils.set_logger('qlearning.log')
weightsFile = open("models/qlearning.weights", "a")
mdp = ActMDP()
qlearning(mdp)
sp, r = mdp.sampleSuccReward(s, a)
done = mdp.isEnd(sp)[0]
score += r
if done:
break
s = sp
dev_scores_window.append(score)
dev_mean_score = np.mean(dev_scores_window)
logging.info("Epoch no {}: dev_mean_score: {:.2f}".format(
num_epoch, dev_mean_score))
if dev_mean_score > best_score:
agent.save(num_epoch, dev_mean_score)
best_score = dev_mean_score
utils.set_logger('logs/train.log')
# run python3 dqn.py or python3 dqn.py --restore best or python3 dqn.py -nn cnn
parser = argparse.ArgumentParser()
parser.add_argument('--nn', default='dnn', help="dnn or cnn")
parser.add_argument(
'--restore',
default=None,
help="Optional, file in models containing weights to reload before training"
)
args = parser.parse_args()
mdp = ActMDP()
train_dqn(mdp, args)