[WIP] Improve training of DQN tutorial

_static/img/reinforcement_learning_diagram.drawio

@@ -0,0 +1 @@
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intermediate_source/reinforcement_q_learning.py

@@ -6,7 +6,7 @@
 
 
 This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent
-on the CartPole-v0 task from the `OpenAI Gym <https://www.gymlibrary.dev/>`__.
+on the CartPole-v1 task from the `OpenAI Gym <https://www.gymlibrary.dev/>`__.
 
 **Task**
 
@@ -30,35 +30,30 @@
 
 The CartPole task is designed so that the inputs to the agent are 4 real
 values representing the environment state (position, velocity, etc.).
-However, neural networks can solve the task purely by looking at the
-scene, so we'll use a patch of the screen centered on the cart as an
-input. Because of this, our results aren't directly comparable to the
-ones from the official leaderboard - our task is much harder.
-Unfortunately this does slow down the training, because we have to
-render all the frames.
+We take these 4 inputs without any scaling and pass them through a 
+small fully-connected network with 2 outputs, one for each action. 
+The network is trained to predict the expected value for each action, 
+given the input state. The action with the highest expected value is 
+then chosen.
 
-Strictly speaking, we will present the state as the difference between
-the current screen patch and the previous one. This will allow the agent
-to take the velocity of the pole into account from one image.
 
 **Packages**
 
 
 First, let's import needed packages. Firstly, we need
 `gym <https://github.com/openai/gym>`__ for the environment
+Install by using `pip`. If you are running this in Google colab, run:
 
 .. code-block:: bash
 
    %%bash
    pip3 install gym[classic_control]
-
+   
 We'll also use the following from PyTorch:
 
 -  neural networks (``torch.nn``)
 -  optimization (``torch.optim``)
 -  automatic differentiation (``torch.autograd``)
--  utilities for vision tasks (``torchvision`` - `a separate
-   package <https://github.com/pytorch/vision>`__).
 
 """
 
@@ -70,19 +65,18 @@
 import matplotlib.pyplot as plt
 from collections import namedtuple, deque
 from itertools import count
-from PIL import Image
 
 import torch
 import torch.nn as nn
 import torch.optim as optim
 import torch.nn.functional as F
-import torchvision.transforms as T
-
 
-if gym.__version__ < '0.26':
-    env = gym.make('CartPole-v0', new_step_api=True, render_mode='single_rgb_array').unwrapped
+if gym.__version__[:4] == '0.26':
+    env = gym.make('CartPole-v1')
+elif gym.__version__[:4] == '0.25':
+    env = gym.make('CartPole-v1', new_step_api=True)
 else:
-    env = gym.make('CartPole-v0', render_mode='rgb_array').unwrapped
+    raise ImportError(f"Requires gym v25 or v26, actual version: {gym.__version__}")
 
 # set up matplotlib
 is_ipython = 'inline' in matplotlib.get_backend()
@@ -152,9 +146,11 @@ def __len__(self):
 # :math:`R_{t_0} = \sum_{t=t_0}^{\infty} \gamma^{t - t_0} r_t`, where
 # :math:`R_{t_0}` is also known as the *return*. The discount,
 # :math:`\gamma`, should be a constant between :math:`0` and :math:`1`
-# that ensures the sum converges. It makes rewards from the uncertain far
-# future less important for our agent than the ones in the near future
-# that it can be fairly confident about.
+# that ensures the sum converges. A lower :math:`\gamma` makes 
+# rewards from the uncertain far future less important for our agent 
+# than the ones in the near future that it can be fairly confident 
+# about. It also encourages agents to collect reward closer in time 
+# than equivalent rewards temporally future away.
 #
 # The main idea behind Q-learning is that if we had a function
 # :math:`Q^*: State \times Action \rightarrow \mathbb{R}`, that could tell
@@ -177,7 +173,7 @@ def __len__(self):
 # The difference between the two sides of the equality is known as the
 # temporal difference error, :math:`\delta`:
 #
-# .. math:: \delta = Q(s, a) - (r + \gamma \max_a Q(s', a))
+# .. math:: \delta = Q(s, a) - (r + \gamma \max_a' Q(s', a))
 #
 # To minimise this error, we will use the `Huber
 # loss <https://en.wikipedia.org/wiki/Huber_loss>`__. The Huber loss acts
@@ -211,86 +207,18 @@ def __len__(self):
 
 class DQN(nn.Module):
 
-    def __init__(self, h, w, outputs):
+    def __init__(self, n_observations, n_actions):
         super(DQN, self).__init__()
-        self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2)
-        self.bn1 = nn.BatchNorm2d(16)
-        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2)
-        self.bn2 = nn.BatchNorm2d(32)
-        self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2)
-        self.bn3 = nn.BatchNorm2d(32)
-
-        # Number of Linear input connections depends on output of conv2d layers
-        # and therefore the input image size, so compute it.
-        def conv2d_size_out(size, kernel_size = 5, stride = 2):
-            return (size - (kernel_size - 1) - 1) // stride  + 1
-        convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w)))
-        convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h)))
-        linear_input_size = convw * convh * 32
-        self.head = nn.Linear(linear_input_size, outputs)
+        self.layer1 = nn.Linear(n_observations, 128)
+        self.layer2 = nn.Linear(128, 128)
+        self.layer3 = nn.Linear(128, n_actions)
 
     # Called with either one element to determine next action, or a batch
     # during optimization. Returns tensor([[left0exp,right0exp]...]).
     def forward(self, x):
-        x = x.to(device)
-        x = F.relu(self.bn1(self.conv1(x)))
-        x = F.relu(self.bn2(self.conv2(x)))
-        x = F.relu(self.bn3(self.conv3(x)))
-        return self.head(x.view(x.size(0), -1))
-
-
-######################################################################
-# Input extraction
-# ^^^^^^^^^^^^^^^^
-#
-# The code below are utilities for extracting and processing rendered
-# images from the environment. It uses the ``torchvision`` package, which
-# makes it easy to compose image transforms. Once you run the cell it will
-# display an example patch that it extracted.
-#
-
-resize = T.Compose([T.ToPILImage(),
-                    T.Resize(40, interpolation=Image.CUBIC),
-                    T.ToTensor()])
-
-
-def get_cart_location(screen_width):
-    world_width = env.x_threshold * 2
-    scale = screen_width / world_width
-    return int(env.state[0] * scale + screen_width / 2.0)  # MIDDLE OF CART
-
-def get_screen():
-    # Returned screen requested by gym is 400x600x3, but is sometimes larger
-    # such as 800x1200x3. Transpose it into torch order (CHW).
-    screen = env.render().transpose((2, 0, 1))
-    # Cart is in the lower half, so strip off the top and bottom of the screen
-    _, screen_height, screen_width = screen.shape
-    screen = screen[:, int(screen_height*0.4):int(screen_height * 0.8)]
-    view_width = int(screen_width * 0.6)
-    cart_location = get_cart_location(screen_width)
-    if cart_location < view_width // 2:
-        slice_range = slice(view_width)
-    elif cart_location > (screen_width - view_width // 2):
-        slice_range = slice(-view_width, None)
-    else:
-        slice_range = slice(cart_location - view_width // 2,
-                            cart_location + view_width // 2)
-    # Strip off the edges, so that we have a square image centered on a cart
-    screen = screen[:, :, slice_range]
-    # Convert to float, rescale, convert to torch tensor
-    # (this doesn't require a copy)
-    screen = np.ascontiguousarray(screen, dtype=np.float32) / 255
-    screen = torch.from_numpy(screen)
-    # Resize, and add a batch dimension (BCHW)
-    return resize(screen).unsqueeze(0)
-
-
-env.reset()
-plt.figure()
-plt.imshow(get_screen().cpu().squeeze(0).permute(1, 2, 0).numpy(),
-           interpolation='none')
-plt.title('Example extracted screen')
-plt.show()
+        x = F.relu(self.layer1(x))
+        x = F.relu(self.layer2(x))
+        return self.layer3(x)
 
 
 ######################################################################
@@ -315,28 +243,35 @@ def get_screen():
 #    episode.
 #
 
+# BATCH_SIZE is the number of transitions sampled from the replay buffer
+# GAMMA is the discount factor as mentioned in the previous section
+# EPS_START is the starting value of epsilon
+# EPS_END is the final value of epsilon
+# EPS_DECAY controls the rate of exponential decay of epsilon, higher means a slower decay
+# TAU is the update rate of the target network
+# LR is the learning rate of the AdamW optimizer
 BATCH_SIZE = 128
-GAMMA = 0.999
+GAMMA = 0.99
 EPS_START = 0.9
 EPS_END = 0.05
-EPS_DECAY = 200
-TARGET_UPDATE = 10
-
-# Get screen size so that we can initialize layers correctly based on shape
-# returned from AI gym. Typical dimensions at this point are close to 3x40x90
-# which is the result of a clamped and down-scaled render buffer in get_screen()
-init_screen = get_screen()
-_, _, screen_height, screen_width = init_screen.shape
+EPS_DECAY = 1000
+TAU = 0.005
+LR = 1e-4
 
 # Get number of actions from gym action space
 n_actions = env.action_space.n
-
-policy_net = DQN(screen_height, screen_width, n_actions).to(device)
-target_net = DQN(screen_height, screen_width, n_actions).to(device)
+# Get the number of state observations
+if gym.__version__[:4] == '0.26':
+    state, _ = env.reset()
+elif gym.__version__[:4] == '0.25':
+    state, _ = env.reset(return_info=True)
+n_observations = len(state)
+
+policy_net = DQN(n_observations, n_actions).to(device)
+target_net = DQN(n_observations, n_actions).to(device)
 target_net.load_state_dict(policy_net.state_dict())
-target_net.eval()
 
-optimizer = optim.RMSprop(policy_net.parameters())
+optimizer = optim.AdamW(policy_net.parameters(), lr=LR, amsgrad=True)
 memory = ReplayMemory(10000)
 
 
@@ -356,14 +291,14 @@ def select_action(state):
             # found, so we pick action with the larger expected reward.
             return policy_net(state).max(1)[1].view(1, 1)
     else:
-        return torch.tensor([[random.randrange(n_actions)]], device=device, dtype=torch.long)
+        return torch.tensor([[env.action_space.sample()]], device=device, dtype=torch.long)
 
 
 episode_durations = []
 
 
 def plot_durations():
-    plt.figure(2)
+    plt.figure(1)
     plt.clf()
     durations_t = torch.tensor(episode_durations, dtype=torch.float)
     plt.title('Training...')
@@ -394,10 +329,9 @@ def plot_durations():
 # :math:`V(s_{t+1}) = \max_a Q(s_{t+1}, a)`, and combines them into our
 # loss. By definition we set :math:`V(s) = 0` if :math:`s` is a terminal
 # state. We also use a target network to compute :math:`V(s_{t+1})` for
-# added stability. The target network has its weights kept frozen most of
-# the time, but is updated with the policy network's weights every so often.
-# This is usually a set number of steps but we shall use episodes for
-# simplicity.
+# added stability. The target network is updated at every step with a 
+# `soft update <https://arxiv.org/pdf/1509.02971.pdf>`__ controlled by 
+# the hyperparameter ``TAU``, which was previously defined.
 #
 
 def optimize_model():
@@ -430,7 +364,8 @@ def optimize_model():
     # This is merged based on the mask, such that we'll have either the expected
     # state value or 0 in case the state was final.
     next_state_values = torch.zeros(BATCH_SIZE, device=device)
-    next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()
+    with torch.no_grad():
+        next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0]
     # Compute the expected Q values
     expected_state_action_values = (next_state_values * GAMMA) + reward_batch
 
@@ -441,44 +376,49 @@ def optimize_model():
     # Optimize the model
     optimizer.zero_grad()
     loss.backward()
-    for param in policy_net.parameters():
-        param.grad.data.clamp_(-1, 1)
+    # In-place gradient clipping
+    torch.nn.utils.clip_grad_value_(policy_net.parameters(), 100)
     optimizer.step()
 
 
 ######################################################################
 #
 # Below, you can find the main training loop. At the beginning we reset
-# the environment and initialize the ``state`` Tensor. Then, we sample
-# an action, execute it, observe the next screen and the reward (always
+# the environment and obtain the initial ``state`` Tensor. Then, we sample
+# an action, execute it, observe the next state and the reward (always
 # 1), and optimize our model once. When the episode ends (our model
 # fails), we restart the loop.
 #
-# Below, `num_episodes` is set small. You should download
-# the notebook and run lot more epsiodes, such as 300+ for meaningful
-# duration improvements.
+# Below, `num_episodes` is set to 600 if a GPU is available, otherwise 50 
+# episodes are scheduled so training does not take too long. However, 50 
+# episodes is insufficient for to observe good performance on cartpole.
+# You should see the model constantly achieve 500 steps within 600 training 
+# episodes. Training RL agents can be a noisy process, so restarting training
+# can produce better results if convergence is not observed.
 #
 
-num_episodes = 50
+if torch.cuda.is_available():
+    num_episodes = 600
+else:
+    num_episodes = 50
+
 for i_episode in range(num_episodes):
-    # Initialize the environment and state
-    env.reset()
-    last_screen = get_screen()
-    current_screen = get_screen()
-    state = current_screen - last_screen
+    # Initialize the environment and get it's state
+    if gym.__version__[:4] == '0.26':
+        state, _ = env.reset()
+    elif gym.__version__[:4] == '0.25':
+        state, _ = env.reset(return_info=True)
+    state = torch.tensor(state, dtype=torch.float32, device=device).unsqueeze(0)
     for t in count():
-        # Select and perform an action
         action = select_action(state)
-        _, reward, done, _, _ = env.step(action.item())
+        observation, reward, terminated, truncated, _ = env.step(action.item())
         reward = torch.tensor([reward], device=device)
+        done = terminated or truncated
 
-        # Observe new state
-        last_screen = current_screen
-        current_screen = get_screen()
-        if not done:
-            next_state = current_screen - last_screen
-        else:
+        if terminated:
             next_state = None
+        else:
+            next_state = torch.tensor(observation, dtype=torch.float32, device=device).unsqueeze(0)
 
         # Store the transition in memory
         memory.push(state, action, next_state, reward)
@@ -488,18 +428,21 @@ def optimize_model():
 
         # Perform one step of the optimization (on the policy network)
         optimize_model()
+
+        # Soft update of the target network's weights
+        # θ′ ← τ θ + (1 −τ )θ′
+        target_net_state_dict = target_net.state_dict()
+        policy_net_state_dict = policy_net.state_dict()
+        for key in policy_net_state_dict:
+            target_net_state_dict[key] = policy_net_state_dict[key]*TAU + target_net_state_dict[key]*(1-TAU)
+        target_net.load_state_dict(target_net_state_dict)
+
         if done:
             episode_durations.append(t + 1)
             plot_durations()
             break
 
-        # Update the target network, copying all weights and biases in DQN
-        if t % TARGET_UPDATE == 0:
-            target_net.load_state_dict(policy_net.state_dict())
-
 print('Complete')
-env.render()
-env.close()
 plt.ioff()
 plt.show()
 
@@ -512,6 +455,6 @@ def optimize_model():
 # step sample from the gym environment. We record the results in the
 # replay memory and also run optimization step on every iteration.
 # Optimization picks a random batch from the replay memory to do training of the
-# new policy. "Older" target_net is also used in optimization to compute the
-# expected Q values; it is updated occasionally to keep it current.
+# new policy. The "older" target_net is also used in optimization to compute the
+# expected Q values. A soft update of its weights are performed at every step.
 #