416 lines
10 KiB
Python
Executable File
416 lines
10 KiB
Python
Executable File
import torch
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import gymnasium as gym
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import random
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import math
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import time
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from collections import deque
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from itertools import count
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from collections import namedtuple
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Transition = namedtuple(
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"Transition",
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(
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"state",
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"action",
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"next_state",
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"reward"
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)
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)
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class Agent:
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def __init__(
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self,
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*,
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## Misc parameters
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#
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# Computation backend. Usually "cpu" or "gpu."
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# Automatic selection if left as None.
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# It's best to leave this as None.
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compute_device = None,
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#
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# Gymnasium environment name.
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env_name = "CartPole-v1",
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## Modules
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network,
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## Hyperparameters
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#
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# BATCH_SIZE is the of batch we should train on, sampled from memory
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# GAMMA is the discount factor for optimization
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BATCH_SIZE = 128,
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GAMMA = 0.99,
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# Learning rate of target_net.
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# Controls how soft our soft update is.
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#
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# Should be between 0 and 1.
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# Large values
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# Small values do the opposite.
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#
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# A value of one makes target_net
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# change at the same rate as policy_net.
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#
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# A value of zero makes target_net
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# not change at all.
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TAU = 0.005,
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# Optimizer learning rate.
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OPT_LR = 1e-4,
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# Epsilon-greedy parameters
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#
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# Original docs:
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# EPS_START is the starting value of epsilon
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# EPS_END is the final value of epsilon
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# EPS_DECAY controls the rate of exponential decay of epsilon, higher means a slower decay
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EPS_START = 0.9,
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EPS_END = 0.05,
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EPS_DECAY = 1000,
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):
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## Auto-select compute device
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if compute_device is None:
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self.compute_device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu"
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)
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else:
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self.compute_device = compute_device
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## Initialize misc values
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self.steps_done = 0 # How many steps this agent has been trained on
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self.network = network # Network class this agent should use
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self.env = gym.make(env_name) # Gym environment
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self.env_name = env_name
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## Initialize replay memory.
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# This is a deque of util.Transitions.
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self.memory = deque([], maxlen = 10_000)
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## Save model hyperparameters
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self.BATCH_SIZE = BATCH_SIZE
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self.GAMMA = GAMMA
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self.TAU = TAU
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self.OPT_LR = OPT_LR
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self.EPS_START = EPS_START
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self.EPS_END = EPS_END
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self.EPS_DECAY = EPS_DECAY
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## Create networks and optimizer
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# n_actions: size of action space
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# - 2 for cartpole: [0, 1] as "left" and "right"
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#
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# n_observations: size of observation vector
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# - 4 for cartpole:
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# position, velocity,
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# angle, angular velocity
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n_actions = self.env.action_space.n # type: ignore
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state, _ = self.env.reset()
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n_observations = len(state)
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# TODO:
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# What's the difference between these two?
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# What do they do?
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self.policy_net = self.network(n_observations, n_actions).to(self.compute_device)
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self.target_net = self.network(n_observations, n_actions).to(self.compute_device)
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# Both networks should start with the same weights
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self.target_net.load_state_dict(self.policy_net.state_dict())
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## Initialize optimizer.
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self.optimizer = torch.optim.AdamW(
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self.policy_net.parameters(),
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lr = self.OPT_LR,
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amsgrad = True
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)
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def _select_action(self, state):
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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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# Random number 0 <= x < 1
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sample = random.random()
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# Calculate random step threshhold
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eps_threshold = (
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self.EPS_END + (self.EPS_START - self.EPS_END) *
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math.exp(
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-1.0 * self.steps_done /
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self.EPS_DECAY
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)
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)
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if sample > 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 self.policy_net(state).max(1)[1].view(1, 1)
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else:
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return torch.tensor(
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[ [self.env.action_space.sample()] ],
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device = self.compute_device,
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dtype = torch.long
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)
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def _optimize(self):
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if len(self.memory) < self.BATCH_SIZE:
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raise Exception(f"Not enough elements in memory for a batch of {self.BATCH_SIZE}")
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# Get a random sample of transitions
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batch = random.sample(self.memory, self.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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state_batch = torch.cat(batch.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 is.
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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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# If this doesn't make sense, RTFD.
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# Compute Q(s_t, a).
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# - Use policy_net to compute Q(s_t) for each state in the batch.
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# This gives a tensor of [ Q(state, left), Q(state, right) ]
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#
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# - Action batch is a tensor that looks like [ [0], [1], [1], ... ]
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# listing the action that was taken in each transition.
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# 0 => we went left, 1 => we went right.
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#
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# This aligns nicely with the output of policy_net. We use
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# action_batch to index the output of policy_net's prediction.
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#
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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 = self.policy_net(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(
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self.BATCH_SIZE,
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device = self.compute_device
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)
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# Don't compute gradient for operations in this block.
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# If you don't understand what this means, RTFD.
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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] = self.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 * self.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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self.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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self.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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self.optimizer.step()
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def train(
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self,
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# Number of training episodes.
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# Need ~400 to see results.
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num_episodes = 400,
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# If true, print progress
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verbose = False
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) -> list[int]:
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# Returns a list of training episode durations.
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# Good for graphing.
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episode_durations = []
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for ep in range(num_episodes):
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# Reset environment and get game state
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state, _ = self.env.reset()
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state = torch.tensor(
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state,
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dtype = torch.float32,
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device = self.compute_device
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).unsqueeze(0)
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# Iterate until game is over
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for t in count():
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# Select next action
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action = self._select_action(state)
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self.steps_done += 1
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# Perform one step of the environment with this action.
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( next_state, # new state
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reward, # number: reward as a result of action
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terminated, # bool: reached a terminal state (win or loss). If True, must reset.
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truncated, # bool: end of time limit. If true, must reset.
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_
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) = self.env.step(action.item())
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# Conversion
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reward = torch.tensor([reward], device = self.compute_device)
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if terminated:
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# If the environment reached a terminal state,
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# observations are meaningless. Set to None.
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next_state = None
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else:
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# Conversion
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next_state = torch.tensor(
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next_state,
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dtype = torch.float32,
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device = self.compute_device
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).unsqueeze(0)
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# Add this state transition to memory.
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self.memory.append(
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Transition(
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state,
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action,
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next_state,
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reward
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)
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)
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state = next_state
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# Only train the network if we have enough
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# transitions in memory to do so.
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if len(self.memory) >= self.BATCH_SIZE:
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# Run optimizer
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self._optimize()
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# Soft update target_net weights
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target_net_state = self.target_net.state_dict()
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policy_net_state = self.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] * self.TAU +
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target_net_state[key] * (1-self.TAU)
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)
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self.target_net.load_state_dict(target_net_state)
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# Move on to the next episode once we reach
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# a terminal state.
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if (terminated or truncated):
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if verbose:
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print(f"Episode {ep}/{num_episodes}, last duration {t+1}", end="\r" )
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episode_durations.append(t + 1)
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break
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return episode_durations
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def predict(self, state):
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return (
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self.policy_net(state)
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.max(1)[1]
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.view(1, 1)
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.item()
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)
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