def __init__(self, state_size, action_size, random_seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
random.seed(random_seed)
self.device = Utils.getDevice()
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size,
random_seed).to(self.device)
self.actor_target = Actor(state_size, action_size,
random_seed).to(self.device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=LR_ACTOR)
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size,
random_seed).to(self.device)
self.critic_target = Critic(state_size, action_size,
random_seed).to(self.device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise(action_size, random_seed)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
self.steps = 0
def __init__(self, state_size, action_size, num_agents, random_seed):
"""Initialize a MADDPG Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
num_agents (int): number of agents
random_seed (int): random seed
"""
super(Maddpg, self).__init__()
self.state_size = state_size
self.action_size = action_size
self.num_agents = num_agents
self.random_seed = random_seed
# Instantiate Multiple Agent
self.agents = [
Agent(state_size, action_size, random_seed)
for i in range(num_agents)
]
self.device = Utils.getDevice()
