duckietownrl

Author: Paul-antoineLeTolguenec

.gitattributes

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+duckiesim/manual/dataset/expert_data_36591.parquet filter=lfs diff=lfs merge=lfs -text

duckietownrl/__init__.py

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+import functools
+import operator
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+# Implementation of Deep Deterministic Policy Gradients (DDPG)
+# Paper: https://arxiv.org/abs/1509.02971
+
+
+class ActorDense(nn.Module):
+    def __init__(self, state_dim, action_dim, max_action):
+        super(ActorDense, self).__init__()
+
+        state_dim = functools.reduce(operator.mul, state_dim, 1)
+
+        self.l1 = nn.Linear(state_dim, 400)
+        self.l2 = nn.Linear(400, 300)
+        self.l3 = nn.Linear(300, action_dim)
+
+        self.max_action = max_action
+
+        self.tanh = nn.Tanh()
+
+    def forward(self, x):
+        x = F.relu(self.l1(x))
+        x = F.relu(self.l2(x))
+        x = self.max_action * self.tanh(self.l3(x))
+        return x
+
+
+class ActorCNN(nn.Module):
+    def __init__(self, action_dim, max_action):
+        super(ActorCNN, self).__init__()
+
+        # ONLY TRU IN CASE OF DUCKIETOWN:
+        flat_size = 32 * 9 * 14
+
+        self.lr = nn.LeakyReLU()
+        self.tanh = nn.Tanh()
+        self.sigm = nn.Sigmoid()
+
+        self.conv1 = nn.Conv2d(3, 32, 8, stride=2)
+        self.conv2 = nn.Conv2d(32, 32, 4, stride=2)
+        self.conv3 = nn.Conv2d(32, 32, 4, stride=2)
+        self.conv4 = nn.Conv2d(32, 32, 4, stride=1)
+
+        self.bn1 = nn.BatchNorm2d(32)
+        self.bn2 = nn.BatchNorm2d(32)
+        self.bn3 = nn.BatchNorm2d(32)
+        self.bn4 = nn.BatchNorm2d(32)
+
+        self.dropout = nn.Dropout(0.5)
+
+        self.lin1 = nn.Linear(flat_size, 512)
+        self.lin2 = nn.Linear(512, action_dim)
+
+        self.max_action = max_action
+
+    def forward(self, x):
+        x = self.bn1(self.lr(self.conv1(x)))
+        x = self.bn2(self.lr(self.conv2(x)))
+        x = self.bn3(self.lr(self.conv3(x)))
+        x = self.bn4(self.lr(self.conv4(x)))
+        x = x.view(x.size(0), -1)  # flatten
+        x = self.dropout(x)
+        x = self.lr(self.lin1(x))
+
+        # this is the vanilla implementation
+        # but we're using a slightly different one
+        # x = self.max_action * self.tanh(self.lin2(x))
+
+        # because we don't want our duckie to go backwards
+        x = self.lin2(x)
+        x[:, 0] = self.max_action * self.sigm(x[:, 0])  # because we don't want the duckie to go backwards
+        x[:, 1] = self.tanh(x[:, 1])
+
+        return x
+
+
+class CriticDense(nn.Module):
+    def __init__(self, state_dim, action_dim):
+        super(CriticDense, self).__init__()
+
+        state_dim = functools.reduce(operator.mul, state_dim, 1)
+
+        self.l1 = nn.Linear(state_dim, 400)
+        self.l2 = nn.Linear(400 + action_dim, 300)
+        self.l3 = nn.Linear(300, 1)
+
+    def forward(self, x, u):
+        x = F.relu(self.l1(x))
+        x = F.relu(self.l2(torch.cat([x, u], 1)))
+        x = self.l3(x)
+        return x
+
+
+class CriticCNN(nn.Module):
+    def __init__(self, action_dim):
+        super(CriticCNN, self).__init__()
+
+        flat_size = 32 * 9 * 14
+
+        self.lr = nn.LeakyReLU()
+
+        self.conv1 = nn.Conv2d(3, 32, 8, stride=2)
+        self.conv2 = nn.Conv2d(32, 32, 4, stride=2)
+        self.conv3 = nn.Conv2d(32, 32, 4, stride=2)
+        self.conv4 = nn.Conv2d(32, 32, 4, stride=1)
+
+        self.bn1 = nn.BatchNorm2d(32)
+        self.bn2 = nn.BatchNorm2d(32)
+        self.bn3 = nn.BatchNorm2d(32)
+        self.bn4 = nn.BatchNorm2d(32)
+
+        self.dropout = nn.Dropout(0.5)
+
+        self.lin1 = nn.Linear(flat_size, 256)
+        self.lin2 = nn.Linear(256 + action_dim, 128)
+        self.lin3 = nn.Linear(128, 1)
+
+    def forward(self, states, actions):
+        x = self.bn1(self.lr(self.conv1(states)))
+        x = self.bn2(self.lr(self.conv2(x)))
+        x = self.bn3(self.lr(self.conv3(x)))
+        x = self.bn4(self.lr(self.conv4(x)))
+        x = x.view(x.size(0), -1)  # flatten
+        x = self.lr(self.lin1(x))
+        x = self.lr(self.lin2(torch.cat([x, actions], 1)))  # c
+        x = self.lin3(x)
+
+        return x
+
+
+class DDPG(object):
+    def __init__(self, state_dim, action_dim, max_action, net_type):
+        super(DDPG, self).__init__()
+        print("Starting DDPG init")
+        assert net_type in ["cnn", "dense"]
+
+        self.state_dim = state_dim
+
+        if net_type == "dense":
+            self.flat = True
+            self.actor = ActorDense(state_dim, action_dim, max_action).to(device)
+            self.actor_target = ActorDense(state_dim, action_dim, max_action).to(device)
+        else:
+            self.flat = False
+            self.actor = ActorCNN(action_dim, max_action).to(device)
+            self.actor_target = ActorCNN(action_dim, max_action).to(device)
+
+        print("Initialized Actor")
+        self.actor_target.load_state_dict(self.actor.state_dict())
+        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=1e-4)
+        print("Initialized Target+Opt [Actor]")
+        if net_type == "dense":
+            self.critic = CriticDense(state_dim, action_dim).to(device)
+            self.critic_target = CriticDense(state_dim, action_dim).to(device)
+        else:
+            self.critic = CriticCNN(action_dim).to(device)
+            self.critic_target = CriticCNN(action_dim).to(device)
+        print("Initialized Critic")
+        self.critic_target.load_state_dict(self.critic.state_dict())
+        self.critic_optimizer = torch.optim.Adam(self.critic.parameters())
+        print("Initialized Target+Opt [Critic]")
+
+    def predict(self, state):
+
+        # just making sure the state has the correct format, otherwise the prediction doesn't work
+        assert state.shape[0] == 3
+
+        if self.flat:
+            state = torch.FloatTensor(state.reshape(1, -1)).to(device)
+        else:
+            state = torch.FloatTensor(np.expand_dims(state, axis=0)).to(device)
+        return self.actor(state).cpu().data.numpy().flatten()
+
+    def train(self, replay_buffer, iterations, batch_size=64, discount=0.99, tau=0.001):
+
+        for it in range(iterations):
+
+            # Sample replay buffer
+            sample = replay_buffer.sample(batch_size, flat=self.flat)
+            state = torch.FloatTensor(sample["state"]).to(device)
+            action = torch.FloatTensor(sample["action"]).to(device)
+            next_state = torch.FloatTensor(sample["next_state"]).to(device)
+            done = torch.FloatTensor(1 - sample["done"]).to(device)
+            reward = torch.FloatTensor(sample["reward"]).to(device)
+
+            # Compute the target Q value
+            target_Q = self.critic_target(next_state, self.actor_target(next_state))
+            target_Q = reward + (done * discount * target_Q).detach()
+
+            # Get current Q estimate
+            current_Q = self.critic(state, action)
+
+            # Compute critic loss
+            critic_loss = F.mse_loss(current_Q, target_Q)
+
+            # Optimize the critic
+            self.critic_optimizer.zero_grad()
+            critic_loss.backward()
+            self.critic_optimizer.step()
+
+            # Compute actor loss
+            actor_loss = -self.critic(state, self.actor(state)).mean()
+
+            # Optimize the actor
+            self.actor_optimizer.zero_grad()
+            actor_loss.backward()
+            self.actor_optimizer.step()
+
+            # Update the frozen target models
+            for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
+                target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
+
+            for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
+                target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
+
+    def save(self, filename, directory):
+        print("Saving to {}/{}_[actor|critic].pth".format(directory, filename))
+        torch.save(self.actor.state_dict(), "{}/{}_actor.pth".format(directory, filename))
+        print("Saved Actor")
+        torch.save(self.critic.state_dict(), "{}/{}_critic.pth".format(directory, filename))
+        print("Saved Critic")
+
+    def load(self, filename, directory):
+        self.actor.load_state_dict(
+            torch.load("{}/{}_actor.pth".format(directory, filename), map_location=device)
+        )
+        self.critic.load_state_dict(
+            torch.load("{}/{}_critic.pth".format(directory, filename), map_location=device)
+        )