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cli.py
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cli.py
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import argparse
def cli_train():
parser = argparse.ArgumentParser(description="Training options")
### Context
parser.add_argument(
"--exp",
type=str,
required=True,
help="Experiment name",
)
parser.add_argument(
"--no_wandb",
action="store_true",
help="Add to prevent logging to wandb",
)
parser.add_argument(
"--render",
action="store_true",
help="Add to generate a visual render of the simulation",
)
parser.add_argument(
"--render_after",
type=int,
default=-1,
help="Delay in time steps before rendering",
)
parser.add_argument(
"--save_actor_name",
type=str,
default=None,
help="Name to store the actor agent after training",
)
parser.add_argument(
"--nb_inter_saving_actor",
type=int,
default=0,
help="Number of intermediate times the actor is saved during training.",
)
### Environment
parser.add_argument(
"--nb_agents",
type=int,
default=-1,
help="Number of agents (TCLs)",
)
parser.add_argument(
"--env_seed",
type=int,
default=1,
help="Environment seed",
)
parser.add_argument(
"--time_step",
type=int,
default=-1,
help="Time step in seconds",
)
## Reward
parser.add_argument(
"--alpha_temp",
type=float,
default=-1,
help="Tradeoff parameter for temperature in the loss function: alpha_temp * temperature penalty + alpha_sig * regulation signal penalty.",
)
parser.add_argument(
"--alpha_sig",
type=float,
default=-1,
help="Tradeoff parameter for signal in the loss function: alpha_temp * temperature penalty + alpha_sig * regulation signal penalty.",
)
parser.add_argument(
"--temp_penalty_mode",
type=str,
default="config",
help="Mode of temperature reward.",
)
parser.add_argument(
"--alpha_ind_L2",
type=float,
default=-1,
help="Coefficient of independant L2 in mixture temperature loss",
)
parser.add_argument(
"--alpha_common_L2",
type=float,
default=-1,
help="Coefficient of common L2 in mixture temperature loss",
)
parser.add_argument(
"--alpha_common_max",
type=float,
default=-1,
help="Coefficient of common_max in mixture temperature loss",
)
## Simulator
# Outdoors
parser.add_argument(
"--OD_temp_mode",
type=str,
default="config",
help="Mode of outdoors temperature.",
)
parser.add_argument(
"--no_solar_gain",
action="store_true",
help="Removes the solar gain from the simulation.",
)
# House and HVAC
parser.add_argument(
"--cooling_capacity",
type=int,
default=-1,
help="Default cooling capacity of the HVACs",
)
parser.add_argument(
"--lockout_duration",
type=int,
default=-1,
help="Default AC lockout duration, in seconds",
)
# Noise
parser.add_argument(
"--house_noise_mode",
type=str,
default="config",
help="Mode of noise over house parameters.",
)
parser.add_argument(
"--house_noise_mode_test",
type=str,
default="train",
help="Mode of noise over house parameters for test environment.",
)
parser.add_argument(
"--hvac_noise_mode",
type=str,
default="config",
help="Mode of noise over HVAC parameters.",
)
parser.add_argument(
"--hvac_lockout_noise",
type=int,
default=-1,
help="Noise on the Hvac Lockout duration in second.",
)
parser.add_argument(
"--hvac_noise_mode_test",
type=str,
default="train",
help="Mode of noise over HVAC parameters for test environment.",
)
## Signal
parser.add_argument(
"--signal_mode",
type=str,
default="config",
help="Mode of the noise on the power grid regulation signal simulation. Choices: [none, regular_steps, sinusoidals, config]",
)
parser.add_argument(
"--base_power_mode",
type=str,
default="config",
help="Mode for the base (low frequency) regulation signal simulation. Choices: [constant, interpolation, config]",
)
parser.add_argument(
"--artificial_signal_ratio",
type=float,
default=1.0,
help="Artificially multiply the base signal for experimental purposes.",
)
parser.add_argument(
"--artificial_signal_ratio_range",
type=float,
default=-1,
help="Range from which the base signal is artificially multiplied or divided at every episode during training. Ex: 1 will not modify the signal. 3 will have signal modified between 1/3 and 3 times the base signal.",
)
## State
parser.add_argument(
"--state_day",
default='config',
choices = ['True','False', 'config'],
help="Include day in the state")
parser.add_argument(
"--state_hour",
default='config',
choices = ['True','False', 'config'],
help="Include hour in the state")
parser.add_argument(
"--state_solar_gain",
default='config',
choices = ['True','False', 'config'],
help="Include solar gain in the state")
parser.add_argument(
"--state_thermal",
default='config',
choices = ['True','False', 'config'],
help="Include outdoors temperature, and house thermal parameters, in the state.")
parser.add_argument(
"--state_hvac",
default='config',
choices = ['True','False', 'config'],
help="hvac parameters, in the state.")
parser.add_argument(
"--message_thermal",
default='config',
choices = ['True','False', 'config'],
help="Include themal parameters in messages")
parser.add_argument(
"--message_hvac",
default='config',
choices = ['True','False', 'config'],
help="Include hvac parameters in messages")
### Agent
parser.add_argument(
"--agent_type",
type=str,
required=True,
help="Type of agent (dqn, ppo)",
)
## Agent communication constraints
parser.add_argument(
"--nb_agents_comm",
type=int,
default=-1,
help="Maximal number of agents each agent can communicate with.",
)
parser.add_argument(
"--agents_comm_mode",
type=str,
default="config",
help="Mode for choosing the agents to communicate with. Can be 'neighbours' or 'random'",
)
parser.add_argument(
"--comm_defect_prob",
type=float,
default=-1,
help="Probability of a communication link to be broken.",
)
## PPO agent
# NN architecture
parser.add_argument(
"--layers_critic",
type=str,
default="config",
help="List containing the number of neurons on each layers of the critic NN model",
)
parser.add_argument(
"--layers_actor",
type=str,
default="config",
help="List containing the number of neurons on each layers of the critic NN model",
)
parser.add_argument(
"--layers_both",
type=str,
default="config",
help="List containing the number of neurons on each layers of the critic NN model",
)
# NN initialization
parser.add_argument(
"--net_seed",
type=int,
default=1,
help="Neural network seed",
)
# NN optimization
parser.add_argument(
"--batch_size",
type=int,
default=-1,
help="Batch size",
)
parser.add_argument(
"--lr_critic", type=float, default=-1, help="Learning rate of critic network"
)
parser.add_argument(
"--lr_actor", type=float, default=-1, help="Learning rate of actor network"
)
parser.add_argument(
"--lr_both",
type=float,
default=-1,
help="Equal learning rate of actor and critic networks",
)
# RL optimization
parser.add_argument(
"--gamma", type=float, default=-1, help="Reward discount parameter"
)
parser.add_argument(
"--clip_param", type=float, default=-1, help="PPO loss clipping parameter"
)
parser.add_argument(
"--max_grad_norm", type=float, default=-1, help="PPO gradient norm maximum"
)
parser.add_argument(
"--ppo_update_time",
type=int,
default=-1,
help="Number of update rounds at each epoch",
)
parser.add_argument(
"--zero_eoepisode_return",
choices=["True", "False"],
default="False",
help="Include day in the state",
)
## DDPG agent
parser.add_argument(
"--gumbel_softmax_tau",
type=float,
default=-1,
help="Temperature parameter for gumbel_softmax in the DDPG.",
)
parser.add_argument(
"--DDPG_shared",
type=float,
default=-1,
help="Temperature parameter for gumbel_softmax in the DDPG.",
)
## DQN agent (only those which were not already added in PPO agent)
parser.add_argument(
"--DQNnetwork_layers",
type=str,
default="config",
help="List containing the number of neurons on each layers of the DQN neural network model",
)
parser.add_argument(
"--tau",
type=float,
default=-1,
help="Rate of update of the target network following the policy network.",
)
parser.add_argument(
"--epsilon_decay",
type=float,
default=-1,
help="Decay rate of epsilon-greedy exploration parameter.",
)
parser.add_argument(
"--min_epsilon",
type=float,
default=-1,
help="Minimal value of epsilon-greedy exploration parameter.",
)
parser.add_argument(
"--buffer_capacity", type=int, default=-1, help="Replay buffer capacity"
)
parser.add_argument(
"--lr",
type=float,
default=-1,
help="Learning rate (for DQN and TarMAC)"
)
## TarMAC agent (only those which were not already added in PPO or DQN agent)
parser.add_argument(
"--recurrent_policy",
choices = ['True','False'],
default = 'True',
help="Whether to use a recurrent policy"
)
parser.add_argument(
"--state_size",
type=int,
default=-1,
help="Size of the internal state vector"
)
parser.add_argument(
"--communication_size",
type=int,
default=-1,
help="Size of the communication vector"
)
parser.add_argument(
"--tarmac_communication_mode",
type=str,
default="config",
help="Communication mode for tarmac (can be: 'no_comm', 'from_states_rec_att', 'from_states')"
)
parser.add_argument(
"--comm_num_hops",
type=int,
default=-1,
help="Number of hops (rounds) for the communication"
)
parser.add_argument(
"--value_loss_coef",
type=float,
default=-1,
help="Value loss coefficient"
)
parser.add_argument(
"--entropy_coef",
type=float,
default=-1,
help="Entropy coefficient"
)
parser.add_argument(
"--eps",
type=float,
default=-1,
help="Epsilon for TarMAC optimizer (RMSProp or Adam)"
)
parser.add_argument(
"--alpha",
type=float,
default=-1,
help="Alpha for TarMAC optimizer (RMSProp or Adam)"
)
parser.add_argument(
"--nb_tarmac_updates",
type=int,
default=-1,
help="Number of updates for TarMAC"
)
## TarMAC PPO agent
parser.add_argument(
"--actor_hidden_state_size",
type=int,
default=-1,
help="Size of the hidden state of the actor"
)
parser.add_argument(
"--critic_hidden_layer_size",
type=int,
default=-1,
help="Size of the critic's hidden linear layers"
)
parser.add_argument(
"--with_gru",
choices = ['True','False', 'config'],
default = 'config',
help="Whether to use a GRU in the actor"
)
parser.add_argument(
"--with_comm",
choices = ['True','False', 'config'],
default = 'config',
help="Whether to use communications in the actor (False -> should be like PPO)"
)
parser.add_argument(
"--key_size",
type=int,
default=-1,
help="Size of the key vector"
)
parser.add_argument(
"--number_agents_comm_tarmac",
type=int,
default=-1,
help="Number of agents to communicate with using TarMAC"
)
parser.add_argument(
"--tarmac_comm_mode",
type=str,
default="config",
help="Communication mode for tarmac (can be: 'all', 'neighbours', 'none', 'random_sample')"
)
parser.add_argument(
"--tarmac_comm_defect_prob",
type=float,
default=-1,
help="Probability of a communication link to be broken.",
)
### Training parameters
### Training parameters
parser.add_argument(
"--nb_tr_episodes",
type=int,
default=-1,
help="Number of episodes (environment resets) for training",
)
parser.add_argument(
"--nb_tr_epochs",
type=int,
default=-1,
help="Number of epochs (policy updates) for training",
)
parser.add_argument(
"--nb_tr_logs",
type=int,
default=-1,
help="Number of logging points for training stats",
)
parser.add_argument(
"--nb_test_logs",
type=int,
default=-1,
help="Number of logging points for testing stats (and thus, testing sessions)",
)
parser.add_argument(
"--nb_time_steps",
type=int,
default=-1,
help="Total number of time steps",
)
parser.add_argument(
"--nb_time_steps_test",
type=int,
default=-1,
help="Total number of time steps in an episode at test time",
)
opt = parser.parse_args()
return opt
def cli_deploy(agents_dict):
parser = argparse.ArgumentParser(description="Deployment options")
parser.add_argument(
"--base_power_mode",
type=str,
default="config",
help="Mode for the base (low frequency) regulation signal simulation. Choices: [constant, interpolation, config]",
)
parser.add_argument(
"--agent",
type=str,
choices=agents_dict.keys(),
required=True,
help="Agent for control",
)
parser.add_argument(
"--render_after",
type=int,
default=-1,
help="Delay in time steps before rendering",
)
parser.add_argument(
"--nb_agents",
type=int,
default=1,
help="Number of agents (TCLs)",
)
parser.add_argument(
"--nb_time_steps",
type=int,
default=1000,
help="Number of time steps in an episode",
)
parser.add_argument(
"--nb_logs",
type=int,
default=100,
help="Number of logging points for training stats",
)
parser.add_argument(
"--env_seed",
type=int,
default=1,
help="Environment seed",
)
parser.add_argument(
"--net_seed",
type=int,
default=1,
help="Network and torch seed",
)
parser.add_argument(
"--exp",
type=str,
default="Deploy",
help="Experiment name",
)
parser.add_argument(
"--no_wandb",
action="store_true",
help="Add to prevent logging to wandb",
)
parser.add_argument(
"--render",
action="store_true",
help="Add to generate a visual render of the simulation",
)
parser.add_argument(
"--cooling_capacity",
type=int,
default=-1,
help="Default cooling capacity of the HVACs",
)
parser.add_argument(
"--time_step",
type=int,
default=-1,
help="Time step in seconds",
)
parser.add_argument(
"--lockout_duration",
type=int,
default=-1,
help="Default AC lockout duration, in seconds",
)
parser.add_argument(
"--actor_name", type=str, default=None, help="Name of the trained agent to load"
)
parser.add_argument(
"--signal_mode",
type=str,
default="config",
help="Mode of power grid regulation signal simulation.",
)
parser.add_argument(
"--house_noise_mode",
type=str,
default="config",
help="Mode of noise over house parameters.",
)
parser.add_argument(
"--hvac_noise_mode",
type=str,
default="config",
help="Mode of noise over hvac parameters.",
)
parser.add_argument(
"--hvac_lockout_noise",
type=int,
default=-1,
help="Noise on the Hvac Lockout duration in second.",
)
parser.add_argument(
"--OD_temp_mode",
type=str,
default="config",
help="Mode of outdoors temperature.",
)
parser.add_argument(
"--no_solar_gain",
action="store_true",
help="Removes the solar gain from the simulation.",
)
parser.add_argument(
"--nb_agents_comm",
type=int,
default=-1,
help="Maximal number of agents each agent can communicate with.",
)
parser.add_argument(
"--agents_comm_mode",
type=str,
default="config",
help="Mode for choosing the agents to communicate with. Can be 'neighbours' or 'random'",
)
parser.add_argument(
"--comm_defect_prob",
type=float,
default=-1,
help="Probability of a communication link to be broken.",
)
parser.add_argument(
"--layers_critic",
type=str,
default="config",
help="List containing the number of neurons on each layers of the critic NN model",
)
parser.add_argument(
"--layers_actor",
type=str,
default="config",
help="List containing the number of neurons on each layers of the critic NN model",
)
parser.add_argument(
"--layers_both",
type=str,
default="config",
help="List containing the number of neurons on each layers of the critic NN model",
)
parser.add_argument(
"--start_stats_from",
type=int,
default=0,
help="Number of time steps from which the stats are calculated.",
)
parser.add_argument(
"--MPC_rolling_horizon",
type=int,
default=-1,
help="Duration of the MPC rooling horizon in time step",
)
parser.add_argument(
"--state_day",
default='config',
choices = ['True','False', 'config'],
help="Include day in the state")
parser.add_argument(
"--message_thermal",
default='config',
choices = ['True','False', 'config'],
help="Include themal parameters in messages")
parser.add_argument(
"--message_hvac",
default='config',
choices = ['True','False', 'config'],
help="Include hvac parameters in messages")
parser.add_argument(
"--state_hour",
default='config',
choices = ['True','False', 'config'],
help="Include hour in the state")
parser.add_argument(
"--state_solar_gain",
default='config',
choices = ['True','False', 'config'],
help="Include solar gain in the state")
parser.add_argument(
"--state_thermal",
default='config',
choices = ['True','False', 'config'],
help="Include outdoors temperature, and house thermal parameters, in the state.")
parser.add_argument(
"--state_hvac",
default='config',
choices = ['True','False', 'config'],
help="hvac parameters, in the state.")
parser.add_argument(
"--start_datetime_mode",
default="config",
help="Decide if start date time is 'fixed' or uniformly 'random'.",
)
parser.add_argument(
"--artificial_signal_ratio",
type=float,
default=1.0,
help="Artificially multiply the base signal for experimental purposes.",
)
parser.add_argument(
"--DQNnetwork_layers",
type=str,
default="config",
help="List containing the number of neurons on each layers of the DQN neural network model",
)
parser.add_argument(
"--log_metrics_path",
type=str,
default="",
help="path to which the simulation data is saved as a CSV. If no name is given no saving is done.",
)
## TarMAC PPO
## TarMAC PPO agent
parser.add_argument(
"--actor_hidden_state_size",
type=int,
default=-1,
help="Size of the hidden state of the actor"
)
parser.add_argument(
"--critic_hidden_layer_size",
type=int,
default=-1,
help="Size of the critic's hidden linear layers"
)
parser.add_argument(
"--with_gru",
choices = ['True','False', 'config'],
default = 'config',
help="Whether to use a GRU in the actor"
)
parser.add_argument(
"--with_comm",
choices = ['True','False', 'config'],
default = 'config',
help="Whether to use communications in the actor (False -> should be like PPO)"
)
parser.add_argument(
"--key_size",
type=int,
default=-1,
help="Size of the key vector"
)
parser.add_argument(
"--number_agents_comm_tarmac",
type=int,
default=-1,
help="Number of agents to communicate with using TarMAC"
)
parser.add_argument(
"--tarmac_comm_mode",
type=str,
default="config",
help="Communication mode for tarmac (can be: 'all', 'neighbours', 'none')"
)
parser.add_argument(
"--tarmac_comm_defect_prob",
type=float,
default=-1,
help="Probability of a communication link to be broken.",
)
parser.add_argument(
"--communication_size",
type=int,
default=-1,
help="Size of the communication vector"
)
parser.add_argument(
"--comm_num_hops",
type=int,
default=-1,
help="Number of hops (rounds) for the communication"
)
opt = parser.parse_args()
return opt