added agent examples for testing the code

agents/dqn_agent.py

+import numpy as np
+import random
+from collections import namedtuple, deque
+import os
+from agent.model import QNetwork, QNetwork2
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+import copy
+
+BUFFER_SIZE = int(1e5)  # replay buffer size
+BATCH_SIZE = 512  # minibatch size
+GAMMA = 0.99  # discount factor 0.99
+TAU = 1e-3  # for soft update of target parameters
+LR = 0.5e-4  # learning rate 5
+UPDATE_EVERY = 10  # how often to update the network
+double_dqn = True  # If using double dqn algorithm
+input_channels = 5  # Number of Input channels
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+device = torch.device("cpu")
+print(device)
+
+
+class Agent():
+    """Interacts with and learns from the environment."""
+
+    def __init__(self, state_size, action_size, net_type, seed, double_dqn=True, input_channels=5):
+        """Initialize an Agent object.
+        
+        Params
+        ======
+            state_size (int): dimension of each state
+            action_size (int): dimension of each action
+            seed (int): random seed
+        """
+        self.state_size = state_size
+        self.action_size = action_size
+        self.seed = random.seed(seed)
+        self.version = net_type
+        self.double_dqn = double_dqn
+        # Q-Network
+        if self.version == "Conv":
+            self.qnetwork_local = QNetwork2(state_size, action_size, seed, input_channels).to(device)
+            self.qnetwork_target = copy.deepcopy(self.qnetwork_local)
+        else:
+            self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
+            self.qnetwork_target = copy.deepcopy(self.qnetwork_local)
+
+        self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
+
+        # Replay memory
+        self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
+        # Initialize time step (for updating every UPDATE_EVERY steps)
+        self.t_step = 0
+
+    def save(self, filename):
+        torch.save(self.qnetwork_local.state_dict(), filename + ".local")
+        torch.save(self.qnetwork_target.state_dict(), filename + ".target")
+
+    def load(self, filename):
+        if os.path.exists(filename + ".local"):
+            self.qnetwork_local.load_state_dict(torch.load(filename + ".local"))
+        if os.path.exists(filename + ".target"):
+            self.qnetwork_target.load_state_dict(torch.load(filename + ".target"))
+
+    def step(self, state, action, reward, next_state, done):
+        # Save experience in replay memory
+        self.memory.add(state, action, reward, next_state, done)
+
+        # Learn every UPDATE_EVERY time steps.
+        self.t_step = (self.t_step + 1) % UPDATE_EVERY
+        if self.t_step == 0:
+            # If enough samples are available in memory, get random subset and learn
+            if len(self.memory) > BATCH_SIZE:
+                experiences = self.memory.sample()
+                self.learn(experiences, GAMMA)
+
+    def act(self, state, eps=0.):
+        """Returns actions for given state as per current policy.
+        
+        Params
+        ======
+            state (array_like): current state
+            eps (float): epsilon, for epsilon-greedy action selection
+        """
+        state = torch.from_numpy(state).float().unsqueeze(0).to(device)
+        self.qnetwork_local.eval()
+        with torch.no_grad():
+            action_values = self.qnetwork_local(state)
+        self.qnetwork_local.train()
+
+        # Epsilon-greedy action selection
+        if random.random() > eps:
+            return np.argmax(action_values.cpu().data.numpy())
+        else:
+            return random.choice(np.arange(self.action_size))
+
+    def learn(self, experiences, gamma):
+
+        """Update value parameters using given batch of experience tuples.
+
+        Params
+        ======
+            experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
+            gamma (float): discount factor
+        """
+        states, actions, rewards, next_states, dones = experiences
+
+        # Get expected Q values from local model
+        Q_expected = self.qnetwork_local(states).gather(1, actions)
+
+        if self.double_dqn:
+            # Double DQN
+            q_best_action = self.qnetwork_local(next_states).max(1)[1]
+            Q_targets_next = self.qnetwork_target(next_states).gather(1, q_best_action.unsqueeze(-1))
+        else:
+            # DQN
+            Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(-1)
+
+            # Compute Q targets for current states
+
+        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
+
+        # Compute loss
+        loss = F.mse_loss(Q_expected, Q_targets)
+        # Minimize the loss
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+        # ------------------- update target network ------------------- #
+        self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
+
+    def soft_update(self, local_model, target_model, tau):
+        """Soft update model parameters.
+        θ_target = τ*θ_local + (1 - τ)*θ_target
+
+        Params
+        ======
+            local_model (PyTorch model): weights will be copied from
+            target_model (PyTorch model): weights will be copied to
+            tau (float): interpolation parameter 
+        """
+        for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
+            target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
+
+
+class ReplayBuffer:
+    """Fixed-size buffer to store experience tuples."""
+
+    def __init__(self, action_size, buffer_size, batch_size, seed):
+        """Initialize a ReplayBuffer object.
+
+        Params
+        ======
+            action_size (int): dimension of each action
+            buffer_size (int): maximum size of buffer
+            batch_size (int): size of each training batch
+            seed (int): random seed
+        """
+        self.action_size = action_size
+        self.memory = deque(maxlen=buffer_size)
+        self.batch_size = batch_size
+        self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
+        self.seed = random.seed(seed)
+
+    def add(self, state, action, reward, next_state, done):
+        """Add a new experience to memory."""
+        e = self.experience(np.expand_dims(state, 0), action, reward, np.expand_dims(next_state, 0), done)
+        self.memory.append(e)
+
+    def sample(self):
+        """Randomly sample a batch of experiences from memory."""
+        experiences = random.sample(self.memory, k=self.batch_size)
+
+        states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
+        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)
+        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
+        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(
+            device)
+        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(
+            device)
+
+        return (states, actions, rewards, next_states, dones)
+
+    def __len__(self):
+        """Return the current size of internal memory."""
+        return len(self.memory)

agents/model.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class QNetwork(nn.Module):
+    def __init__(self, state_size, action_size, seed, hidsize1=128, hidsize2=128):
+        super(QNetwork, self).__init__()
+
+        self.fc1_val = nn.Linear(state_size, hidsize1)
+        self.fc2_val = nn.Linear(hidsize1, hidsize2)
+        self.fc3_val = nn.Linear(hidsize2, 1)
+
+        self.fc1_adv = nn.Linear(state_size, hidsize1)
+        self.fc2_adv = nn.Linear(hidsize1, hidsize2)
+        self.fc3_adv = nn.Linear(hidsize2, action_size)
+
+    def forward(self, x):
+        val = F.relu(self.fc1_val(x))
+        val = F.relu(self.fc2_val(val))
+        val = self.fc3_val(val)
+
+        # advantage calculation
+        adv = F.relu(self.fc1_adv(x))
+        adv = F.relu(self.fc2_adv(adv))
+        adv = self.fc3_adv(adv)
+        return val + adv - adv.mean()
+
+
+class QNetwork2(nn.Module):
+    def __init__(self, state_size, action_size, seed, input_channels, hidsize1=128, hidsize2=64):
+        super(QNetwork2, self).__init__()
+        self.conv1 = nn.Conv2d(input_channels, 16, kernel_size=3, stride=1)
+        self.bn1 = nn.BatchNorm2d(16)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=3)
+        self.bn2 = nn.BatchNorm2d(32)
+        self.conv3 = nn.Conv2d(32, 64, kernel_size=5, stride=3)
+        self.bn3 = nn.BatchNorm2d(64)
+
+        self.fc1_val = nn.Linear(6400, hidsize1)
+        self.fc2_val = nn.Linear(hidsize1, hidsize2)
+        self.fc3_val = nn.Linear(hidsize2, 1)
+
+        self.fc1_adv = nn.Linear(6400, hidsize1)
+        self.fc2_adv = nn.Linear(hidsize1, hidsize2)
+        self.fc3_adv = nn.Linear(hidsize2, action_size)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+
+        # value function approximation
+        val = F.relu(self.fc1_val(x.view(x.size(0), -1)))
+        val = F.relu(self.fc2_val(val))
+        val = self.fc3_val(val)
+
+        # advantage calculation
+        adv = F.relu(self.fc1_adv(x.view(x.size(0), -1)))
+        adv = F.relu(self.fc2_adv(adv))
+        adv = self.fc3_adv(adv)
+        return val + adv - adv.mean()

examples/temporary_example.py

@@ -21,12 +21,12 @@ transition_probability = [1.0,  # empty cell - Case 0
 """
 transition_probability = [1.0,  # empty cell - Case 0
                           1.0,  # Case 1 - straight
-                          1.0,  # Case 2 - simple switch
-                          1.0,  # Case 3 - diamond drossing
-                          1.0,  # Case 4 - single slip
-                          1.0,  # Case 5 - double slip
-                          1.0,  # Case 6 - symmetrical
-                          1.0]  # Case 7 - dead end
+                          0.5,  # Case 2 - simple switch
+                          0.2,  # Case 3 - diamond drossing
+                          0.5,  # Case 4 - single slip
+                          0.1,  # Case 5 - double slip
+                          0.2,  # Case 6 - symmetrical
+                          0.01]  # Case 7 - dead end
 
 # Example generate a random rail
 env = RailEnv(width=20,