/
alg_parameters.py
executable file
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/
alg_parameters.py
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import datetime
""" Hyperparameters """
class EnvParameters:
N_AGENTS = 8 # number of agents used in training todo 8
N_ACTIONS = 5
EPISODE_LEN = 256 # maximum episode length in training
FOV_SIZE = 9
WORLD_SIZE = (10, 40) # todo: (10, 40)
OBSTACLE_PROB = (0.0, 0.3) # todo: (0, 0.3)
ACTION_COST = -0.3
IDLE_COST = -0.3
GOAL_REWARD = 0.0
COLLISION_COST = -2
BLOCKING_COST = -1
class TrainingParameters:
lr = 1e-5
GAMMA = 0.95 # discount factor
LAM = 0.95 # For GAE
CLIP_RANGE = 0.2
MAX_GRAD_NORM = 10
ENTROPY_COEF = 0.01
VALUE_COEF = 0.08
POLICY_COEF = 10
VALID_COEF = 0.5
BLOCK_COEF = 0.5
N_EPOCHS = 10
N_ENVS = 32 # number of processes todo 32
N_MAX_STEPS = 3e7 # maximum number of time steps used in training
N_STEPS = 2 ** 8 # number of time steps per process per data collection todo 2**10
MINIBATCH_SIZE = int(2 ** 8) # todo 2**10
DEMONSTRATION_PROB = 0.1 # imitation learning rate todo: 0.1
class NetParameters:
NET_SIZE = 512
NUM_CHANNEL = 4 # number of channels of observations -[FOV_SIZE x FOV_SIZEx NUM_CHANNEL]
GOAL_REPR_SIZE = 12
VECTOR_LEN = 4 # [dx, dy, d total, action t-1]
NUM_NODES = 50 # There are always to many nodes in PRIMAL1 map, if we select PRIMAL2 map, we can decrease the num of nodes
MAX_NEIGHBOR = 50
EMBEDDING_DIM = 512
NUM_FEATURE = 5
NUM_INTENTION_FEATURE = 9
class SetupParameters:
SEED = 1234
USE_GPU_LOCAL = False
USE_GPU_GLOBAL = True
NUM_GPU = 2
class RecordingParameters:
RETRAIN = False
WANDB = True # set as True when real training started
TENSORBOARD = False
TXT_WRITER = True
ENTITY = 'ALPHA_PLUS'
TIME = datetime.datetime.now().strftime('%d-%m-%y%H%M')
EXPERIMENT_PROJECT = 'ALPHA_ICRA'
EXPERIMENT_NAME = 'Intention_frontier_11'
EXPERIMENT_NOTE = 'In this version, there is no FA layer but only 1 layers of self-attention'
SAVE_INTERVAL = 5e5 # interval of saving model0
BEST_INTERVAL = 0 # interval of saving model0 with the best performance
GIF_INTERVAL = 1e6 # interval of saving gif
EVAL_INTERVAL = TrainingParameters.N_ENVS * TrainingParameters.N_STEPS # interval of evaluating training model0
EVAL_EPISODES = 1 # number of episode used in evaluation
RECORD_BEST = False
MODEL_PATH = './models' + '/' + EXPERIMENT_PROJECT + '/' + EXPERIMENT_NAME + TIME
GIFS_PATH = './gifs' + '/' + EXPERIMENT_PROJECT + '/' + EXPERIMENT_NAME + TIME
TEST_GIFS_PATH = './test_gifs' + '/' + EXPERIMENT_PROJECT + '/' + EXPERIMENT_NAME + TIME
SUMMARY_PATH = './summaries' + '/' + EXPERIMENT_PROJECT + '/' + EXPERIMENT_NAME + TIME
TXT_NAME = 'alg.txt'
LOSS_NAME = ['all_loss', 'policy_loss', 'policy_entropy', 'critic_loss', 'valid_loss',
'blocking_loss', 'clipfrac',
'grad_norm', 'advantage']
all_args = {'N_AGENTS': EnvParameters.N_AGENTS, 'N_ACTIONS': EnvParameters.N_ACTIONS,
'EPISODE_LEN': EnvParameters.EPISODE_LEN, 'FOV_SIZE': EnvParameters.FOV_SIZE,
'WORLD_SIZE': EnvParameters.WORLD_SIZE,
'OBSTACLE_PROB': EnvParameters.OBSTACLE_PROB,
'ACTION_COST': EnvParameters.ACTION_COST,
'IDLE_COST': EnvParameters.IDLE_COST, 'GOAL_REWARD': EnvParameters.GOAL_REWARD,
'COLLISION_COST': EnvParameters.COLLISION_COST,
'BLOCKING_COST': EnvParameters.BLOCKING_COST,
'lr': TrainingParameters.lr, 'GAMMA': TrainingParameters.GAMMA, 'LAM': TrainingParameters.LAM,
'CLIPRANGE': TrainingParameters.CLIP_RANGE, 'MAX_GRAD_NORM': TrainingParameters.MAX_GRAD_NORM,
'ENTROPY_COEF': TrainingParameters.ENTROPY_COEF,
'VALUE_COEF': TrainingParameters.VALUE_COEF,
'POLICY_COEF': TrainingParameters.POLICY_COEF,
'VALID_COEF': TrainingParameters.VALID_COEF, 'BLOCK_COEF': TrainingParameters.BLOCK_COEF,
'N_EPOCHS': TrainingParameters.N_EPOCHS, 'N_ENVS': TrainingParameters.N_ENVS,
'N_MAX_STEPS': TrainingParameters.N_MAX_STEPS,
'N_STEPS': TrainingParameters.N_STEPS, 'MINIBATCH_SIZE': TrainingParameters.MINIBATCH_SIZE,
'DEMONSTRATION_PROB': TrainingParameters.DEMONSTRATION_PROB,
'NET_SIZE': NetParameters.NET_SIZE, 'NUM_CHANNEL': NetParameters.NUM_CHANNEL,
'GOAL_REPR_SIZE': NetParameters.GOAL_REPR_SIZE, 'VECTOR_LEN': NetParameters.VECTOR_LEN,
'SEED': SetupParameters.SEED, 'USE_GPU_LOCAL': SetupParameters.USE_GPU_LOCAL,
'USE_GPU_GLOBAL': SetupParameters.USE_GPU_GLOBAL,
'NUM_GPU': SetupParameters.NUM_GPU, 'RETRAIN': RecordingParameters.RETRAIN,
'WANDB': RecordingParameters.WANDB,
'TENSORBOARD': RecordingParameters.TENSORBOARD, 'TXT_WRITER': RecordingParameters.TXT_WRITER,
'ENTITY': RecordingParameters.ENTITY,
'TIME': RecordingParameters.TIME, 'EXPERIMENT_PROJECT': RecordingParameters.EXPERIMENT_PROJECT,
'EXPERIMENT_NAME': RecordingParameters.EXPERIMENT_NAME,
'EXPERIMENT_NOTE': RecordingParameters.EXPERIMENT_NOTE,
'SAVE_INTERVAL': RecordingParameters.SAVE_INTERVAL, "BEST_INTERVAL": RecordingParameters.BEST_INTERVAL,
'GIF_INTERVAL': RecordingParameters.GIF_INTERVAL, 'EVAL_INTERVAL': RecordingParameters.EVAL_INTERVAL,
'EVAL_EPISODES': RecordingParameters.EVAL_EPISODES, 'RECORD_BEST': RecordingParameters.RECORD_BEST,
'MODEL_PATH': RecordingParameters.MODEL_PATH, 'GIFS_PATH': RecordingParameters.GIFS_PATH,
'SUMMARY_PATH': RecordingParameters.SUMMARY_PATH,
'TXT_NAME': RecordingParameters.TXT_NAME}