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- class Agent():
- """Interacts with and learns from the environment."""
- 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
- self.seed = random.seed(random_seed)
- # Actor Network (w/ Target Network)
- self.actor_local = Actor(state_size, action_size, random_seed).to(device)
- self.actor_target = Actor(state_size, action_size, random_seed).to(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(device)
- self.critic_target = Critic(state_size, action_size, random_seed).to(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)
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