493 lines
12 KiB
Python
493 lines
12 KiB
Python
from collections import namedtuple
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from collections import deque
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from pathlib import Path
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import random
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import math
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import json
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import torch
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import shutil
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from celeste_ai import Celeste
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from celeste_ai import DQN
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from celeste_ai import Transition
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from celeste_ai.util.screenshots import ScreenshotManager
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if __name__ == "__main__":
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# Where to read/write model data.
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model_data_root = Path("model_data/current")
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sm = ScreenshotManager(
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# Where PICO-8 saves screenshots.
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# Probably your desktop.
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source = Path("/home/mark/Desktop"),
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pattern = "hackcel_*.png",
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target = model_data_root / "screenshots"
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).clean() # Remove old screenshots
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model_save_path = model_data_root / "model.torch"
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model_archive_dir = model_data_root / "model_archive"
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model_train_log = model_data_root / "train_log"
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model_data_root.mkdir(parents = True, exist_ok = True)
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model_archive_dir.mkdir(parents = True, exist_ok = True)
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compute_device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu"
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)
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# Celeste env properties
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n_observations = len(Celeste.state_number_map)
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n_actions = len(Celeste.action_space)
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# Epsilon-greedy parameters
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# Probability of choosing a random action starts at
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# EPS_START and decays to EPS_END.
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# EPS_DECAY controls the rate of decay.
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EPS_START = 0.9
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EPS_END = 0.02
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EPS_DECAY = 100
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# Bellman equation time-discount factor
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GAMMA = 0.9
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# Train on this many transitions from
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# replay memory each round
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BATCH_SIZE = 100
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# Controls target_net soft update.
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# Should be between 0 and 1.
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TAU = 0.05
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# Optimizer learning rate
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learning_rate = 0.001
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# Save a snapshot of the model every n
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# episodes.
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model_save_interval = 10
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# How many times we've reached each point.
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# This is used to compute epsilon-greedy probability.
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point_counter = [0] * len(Celeste.target_checkpoints[0])
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n_episodes = 0 # Number of episodes we've trained on
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n_steps = 0 # Number of training steps we've completed
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# Create replay memory.
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#
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# Holds <Transition> objects, defined in
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# network.py
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memory = deque([], maxlen=50_000)
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policy_net = DQN(n_observations, n_actions).to(compute_device)
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target_net = DQN(n_observations, n_actions).to(compute_device)
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target_net.load_state_dict(policy_net.state_dict())
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optimizer = torch.optim.AdamW(
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policy_net.parameters(),
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lr = learning_rate,
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amsgrad = True
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)
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if model_save_path.is_file():
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# Load model if one exists
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checkpoint = torch.load(
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model_save_path,
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map_location = compute_device
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)
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policy_net.load_state_dict(checkpoint["policy_state_dict"])
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target_net.load_state_dict(checkpoint["target_state_dict"])
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optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
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memory = checkpoint["memory"]
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n_episodes = checkpoint["n_episodes"]
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n_steps = checkpoint["n_steps"]
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point_counter = checkpoint["point_counter"]
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def save_model(path):
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torch.save({
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# Newtorks
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"policy_state_dict": policy_net.state_dict(),
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"target_state_dict": target_net.state_dict(),
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"optimizer_state_dict": optimizer.state_dict(),
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# Training data
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"memory": memory,
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"point_counter": point_counter,
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"n_episodes": n_episodes,
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"n_steps": n_steps,
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# Hyperparameters,
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# for reference
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"eps_start": EPS_START,
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"eps_end": EPS_END,
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"eps_decay": EPS_DECAY,
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"batch_size": BATCH_SIZE,
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"tau": TAU,
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"learning_rate": learning_rate,
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"gamma": GAMMA
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}, path
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)
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def select_action(state, x) -> int:
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"""
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Select an action using an epsilon-greedy policy.
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Sometimes use our model, sometimes sample one uniformly.
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P(random action) starts at EPS_START and decays to EPS_END.
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Decay rate is controlled by EPS_DECAY.
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"""
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# Calculate random step threshhold
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eps_threshold = (
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EPS_END + (EPS_START - EPS_END) *
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math.exp(-1.0 * x / EPS_DECAY)
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)
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if random.random() > eps_threshold:
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with torch.no_grad():
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# t.max(1) will return the largest column value of each row.
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# second column on max result is index of where max element was
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# found, so we pick action with the larger expected reward.
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return policy_net(state).max(1)[1].view(1, 1).item()
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else:
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return random.randint( 0, n_actions-1 )
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def optimize_model():
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if len(memory) < BATCH_SIZE:
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raise Exception(f"Not enough elements in memory for a batch of {BATCH_SIZE}")
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# Get a random sample of transitions
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batch = random.sample(memory, BATCH_SIZE)
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# Conversion.
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# Transposes batch, turning an array of Transitions
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# into a Transition of arrays.
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batch = Transition(*zip(*batch))
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# Conversion.
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# Combine states, actions, and rewards into their own tensors.
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last_state_batch = torch.cat(batch.last_state)
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action_batch = torch.cat(batch.action)
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reward_batch = torch.cat(batch.reward)
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# Compute a mask of non_final_states.
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# Each element of this tensor corresponds to an element in the batch.
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# True if this is a final state, False if it isn't.
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#
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# We use this to select non-final states later.
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non_final_mask = torch.tensor(
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tuple(map(
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lambda s: s is not None,
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batch.next_state
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))
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)
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non_final_next_states = torch.cat(
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[s for s in batch.next_state if s is not None]
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)
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# How .gather works:
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# if out = a.gather(1, b),
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# out[i, j] = a[ i ][ b[i,j] ]
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#
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# a is "input," b is "index"
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# Compute Q(s_t, a).
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# This gives us a tensor that contains the return we expect to get
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# at that state if we follow the model's advice.
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state_action_values = policy_net(last_state_batch).gather(1, action_batch)
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# Compute V(s_t+1) for all next states.
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# V(s_t+1) = max_a ( Q(s_t+1, a) )
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# = the maximum reward over all possible actions at state s_t+1.
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next_state_values = torch.zeros(BATCH_SIZE, device = compute_device)
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with torch.no_grad():
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# Note the use of non_final_mask here.
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# States that are final do not have their reward set by the line
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# below, so their reward stays at zero.
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#
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# States that are not final get their predicted value
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# set to the best value the model predicts.
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#
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#
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# Expected values of action are selected with the "older" target net,
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# and their best reward (over possible actions) is selected with max(1)[0].
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next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0]
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# TODO: What does this mean?
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# "Compute expected Q values"
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expected_state_action_values = reward_batch + (next_state_values * GAMMA)
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# Compute Huber loss between predicted reward and expected reward.
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# Pytorch is will account for this when we compute the gradient of loss.
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#
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# loss is a single-element tensor (i.e, a scalar).
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criterion = torch.nn.SmoothL1Loss()
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loss = criterion(
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state_action_values,
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expected_state_action_values.unsqueeze(1)
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)
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# We can now run a step of backpropagation on our model.
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# TODO: what does this do?
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#
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# Calling .backward() multiple times will accumulate parameter gradients.
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# Thus, we reset the gradient before each step.
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optimizer.zero_grad()
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# Compute the gradient of loss wrt... something?
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# TODO: what does this do, we never use loss again?!
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loss.backward()
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# Prevent vanishing and exploding gradients.
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# Forces gradients to be in [-clip_value, +clip_value]
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torch.nn.utils.clip_grad_value_( # type: ignore
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policy_net.parameters(),
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clip_value = 100
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)
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# Perform a single optimizer step.
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#
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# Uses the current gradient, which is stored
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# in the .grad attribute of the parameter.
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optimizer.step()
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return loss
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def on_state_before(celeste):
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state = celeste.state
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action = select_action(
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# Put state in a tensor
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torch.tensor(
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[getattr(state, x) for x in Celeste.state_number_map],
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dtype = torch.float32,
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device = compute_device
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).unsqueeze(0),
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# Random action probability is determined by
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# the number of times we've reached the next point.
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point_counter[state.next_point]
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)
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# For manual testing
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#str_action = ""
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#while str_action not in Celeste.action_space:
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# str_action = input("action> ")
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#action = Celeste.action_space.index(str_action)
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print(Celeste.action_space[action])
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celeste.act(action)
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return (
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state, # CelesteState
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action # Integer
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)
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def compute_reward(last_state, state):
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global point_counter
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reward = None
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# No reward if dead
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if state.deaths != 0:
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reward = 0
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# Reward for finishing a stage
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elif state.stage >= 1:
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print("FINISHED STAGE!!")
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# We don't set a fixed reward here because the agent may
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# complete the stage before getting all points.
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# The below line provides extra reward for taking shortcuts.
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reward = state.next_point - last_state.next_point
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reward += 1
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# Add to point counter
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for i in range(last_state.next_point, len(point_counter)):
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point_counter[i] += 1
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# Reward for reaching a checkpoint
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elif last_state.next_point != state.next_point:
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print(f"Got point {state.next_point}")
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reward = state.next_point - last_state.next_point
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# Add to point counter
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for i in range(last_state.next_point, last_state.next_point + reward):
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point_counter[i] += 1
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# No reward otherwise
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else:
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reward = 0
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# Strawberry reward
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# (Will probably break current version of model)
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#if state.berries[state.stage] and not state.berries[state.stage]:
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# print(f"Got stage {state.stage} bonus")
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# reward += 1
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assert reward is not None
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return reward * 10
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def on_state_after(celeste, before_out):
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global n_episodes
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global n_steps
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last_state, action = before_out
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next_state = celeste.state
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dead = next_state.deaths != 0
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done = next_state.stage >= 1
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reward = compute_reward(last_state, next_state)
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if dead:
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next_state = None
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elif done:
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# We don't set the next state to None because
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# the optimization routine forces zero reward
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# for terminal states.
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# Copy last state instead. It's a hack, but it
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# should work.
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next_state = last_state
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# Add this state transition to memory.
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memory.append(
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Transition(
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# last state
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torch.tensor(
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[getattr(last_state, x) for x in Celeste.state_number_map],
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dtype = torch.float32,
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device = compute_device
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).unsqueeze(0),
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# action
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torch.tensor(
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[[ action ]],
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device = compute_device,
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dtype = torch.long
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),
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# next state
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# None if dead or done.
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torch.tensor(
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[getattr(next_state, x) for x in Celeste.state_number_map],
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dtype = torch.float32,
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device = compute_device
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).unsqueeze(0) if next_state is not None else None,
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# reward
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torch.tensor(
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[reward],
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device = compute_device
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)
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)
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)
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print("==> ", reward)
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print("")
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# Perform a training step
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loss = None
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if len(memory) >= BATCH_SIZE:
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n_steps += 1
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loss = optimize_model()
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# Soft update target_net weights
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target_net_state = target_net.state_dict()
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policy_net_state = policy_net.state_dict()
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for key in policy_net_state:
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target_net_state[key] = (
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policy_net_state[key] * TAU +
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target_net_state[key] * (1-TAU)
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)
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target_net.load_state_dict(target_net_state)
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# Move on to the next episode and run
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# housekeeping tasks.
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if (dead or done):
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s = celeste.state
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n_episodes += 1
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# Move screenshots
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sm.move(
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number = n_episodes,
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overwrite = True
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)
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# Log this episode
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with model_train_log.open("a") as f:
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f.write(json.dumps({
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"n_episodes": n_episodes,
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"n_steps": n_steps,
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"checkpoints": s.next_point,
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"loss": None if loss is None else loss.item(),
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"done": done
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}) + "\n")
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# Save a snapshot
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if n_episodes % model_save_interval == 0:
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save_model(model_archive_dir / f"{n_episodes}.torch")
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shutil.copy(model_archive_dir / f"{n_episodes}.torch", model_save_path)
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print("Game over. Resetting.")
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celeste.reset()
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if __name__ == "__main__":
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c = Celeste(
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"resources/pico-8/linux/pico8"
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)
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c.update_loop(
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on_state_before,
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on_state_after
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)
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