mirror of
https://github.com/illiumst/marl-factory-grid.git
synced 2025-05-23 07:16:44 +02:00
282 lines
12 KiB
Python
282 lines
12 KiB
Python
from typing import NamedTuple, Union
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from collections import deque
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import numpy as np
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import random
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Experience(NamedTuple):
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# can be use for a single (s_t, a, r s_{t+1}) tuple
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# or for a batch of tuples
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observation: np.ndarray
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next_observation: np.ndarray
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action: np.ndarray
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reward: Union[float, np.ndarray]
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done : Union[bool, np.ndarray]
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class BaseBuffer:
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def __init__(self, size: int):
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self.size = size
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self.experience = deque(maxlen=size)
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def __len__(self):
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return len(self.experience)
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def add(self, experience):
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self.experience.append(experience)
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def sample(self, k, cer=4):
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sample = random.choices(self.experience, k=k-cer)
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for i in range(cer): sample += [self.experience[-i]]
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observations = torch.stack([torch.from_numpy(e.observation) for e in sample], 0).float()
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next_observations = torch.stack([torch.from_numpy(e.next_observation) for e in sample], 0).float()
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actions = torch.tensor([e.action for e in sample]).long()
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rewards = torch.tensor([e.reward for e in sample]).float().view(-1, 1)
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dones = torch.tensor([e.done for e in sample]).float().view(-1, 1)
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return Experience(observations, next_observations, actions, rewards, dones)
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class BaseDDQN(nn.Module):
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def __init__(self):
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super(BaseDDQN, self).__init__()
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self.net = nn.Sequential(
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nn.Flatten(),
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nn.Linear(3*5*5, 64),
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nn.ELU(),
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nn.Linear(64, 64),
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nn.ELU()
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)
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self.value_head = nn.Linear(64, 1)
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self.advantage_head = nn.Linear(64, 9)
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def act(self, x) -> np.ndarray:
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with torch.no_grad():
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action = self.forward(x).max(-1)[1].numpy()
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return action
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def forward(self, x):
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features = self.net(x)
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advantages = self.advantage_head(features)
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values = self.value_head(features)
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return values + (advantages - advantages.mean())
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class BaseDQN(nn.Module):
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def __init__(self):
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super(BaseDQN, self).__init__()
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self.net = nn.Sequential(
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nn.Flatten(),
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nn.Linear(3*5*5, 64),
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nn.ELU(),
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nn.Linear(64, 64),
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nn.ELU(),
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nn.Linear(64, 9)
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)
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def act(self, x) -> np.ndarray:
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with torch.no_grad():
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action = self.forward(x).max(-1)[1].numpy()
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return action
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def forward(self, x):
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return self.net(x)
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def soft_update(local_model, target_model, tau):
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# taken from https://github.com/BY571/Munchausen-RL/blob/master/M-DQN.ipynb
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for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
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target_param.data.copy_(tau*local_param.data + (1.-tau)*target_param.data)
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class BaseQlearner:
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def __init__(self, q_net, target_q_net, env, buffer, target_update, eps_end, n_agents=1,
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gamma=0.99, train_every_n_steps=4, n_grad_steps=1, tau=1.0, max_grad_norm=10,
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exploration_fraction=0.2, batch_size=64, lr=1e-4, reg_weight=0.0):
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self.q_net = q_net
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self.target_q_net = target_q_net
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#self.q_net.apply(self.weights_init)
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self.target_q_net.eval()
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soft_update(self.q_net, self.target_q_net, tau=1.0)
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self.env = env
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self.buffer = buffer
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self.target_update = target_update
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self.eps = 1.
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self.eps_end = eps_end
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self.exploration_fraction = exploration_fraction
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self.batch_size = batch_size
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self.gamma = gamma
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self.train_every_n_steps = train_every_n_steps
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self.n_grad_steps = n_grad_steps
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self.lr = lr
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self.tau = tau
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self.reg_weight = reg_weight
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self.n_agents = n_agents
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self.device = 'cpu'
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self.optimizer = torch.optim.AdamW(self.q_net.parameters(), lr=self.lr)
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self.max_grad_norm = max_grad_norm
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self.running_reward = deque(maxlen=5)
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self.running_loss = deque(maxlen=5)
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self._n_updates = 0
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def to(self, device):
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self.device = device
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return self
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@staticmethod
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def weights_init(module, activation='leaky_relu'):
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if isinstance(module, (nn.Linear, nn.Conv2d)):
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nn.init.xavier_normal_(module.weight, gain=torch.nn.init.calculate_gain(activation))
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if module.bias is not None:
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module.bias.data.fill_(0.0)
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def anneal_eps(self, step, n_steps):
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fraction = min(float(step) / int(self.exploration_fraction*n_steps), 1.0)
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self.eps = 1 + fraction * (self.eps_end - 1)
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def get_action(self, obs) -> Union[int, np.ndarray]:
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o = torch.from_numpy(obs).unsqueeze(0) if self.n_agents <= 1 else torch.from_numpy(obs)
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if np.random.rand() > self.eps:
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action = self.q_net.act(o.float())
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else:
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action = np.array([self.env.action_space.sample() for _ in range(self.n_agents)])
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return action
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def learn(self, n_steps):
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step = 0
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while step < n_steps:
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obs, done = self.env.reset(), False
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total_reward = 0
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while not done:
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action = self.get_action(obs)
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next_obs, reward, done, info = self.env.step(action if not len(action) == 1 else action[0])
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experience = Experience(observation=obs, next_observation=next_obs, action=action, reward=reward, done=done) # do we really need to copy?
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self.buffer.add(experience)
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# end of step routine
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obs = next_obs
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step += 1
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total_reward += reward
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self.anneal_eps(step, n_steps)
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if step % self.train_every_n_steps == 0:
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self.train()
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self._n_updates += 1
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if step % self.target_update == 0:
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print('UPDATE')
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soft_update(self.q_net, self.target_q_net, tau=self.tau)
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self.running_reward.append(total_reward)
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if step % 10 == 0:
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print(f'Step: {step} ({(step/n_steps)*100:.2f}%)\tRunning reward: {sum(list(self.running_reward))/len(self.running_reward):.2f}\t'
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f' eps: {self.eps:.4f}\tRunning loss: {sum(list(self.running_loss))/len(self.running_loss):.4f}\tUpdates:{self._n_updates}')
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def _training_routine(self, obs, next_obs, action):
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current_q_values = self.q_net(obs)
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current_q_values = torch.gather(current_q_values, dim=-1, index=action)
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next_q_values_raw = self.target_q_net(next_obs).max(dim=-1)[0].reshape(-1, 1).detach()
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#print(current_q_values.shape, next_q_values_raw.shape)
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return current_q_values, next_q_values_raw
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def _backprop_loss(self, loss):
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# log loss
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self.running_loss.append(loss.item())
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# Optimize the model
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self.optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_norm_(self.q_net.parameters(), self.max_grad_norm)
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self.optimizer.step()
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def train(self):
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if len(self.buffer) < self.batch_size: return
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for _ in range(self.n_grad_steps):
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experience = self.buffer.sample(self.batch_size, cer=self.train_every_n_steps)
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if self.n_agents <= 1:
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pred_q, target_q_raw = self._training_routine(experience.observation,
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experience.next_observation,
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experience.action)
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else:
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pred_q, target_q_raw = torch.zeros((self.batch_size, 1)), torch.zeros((self.batch_size, 1))
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for agent_i in range(self.n_agents):
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q_values, next_q_values_raw = self._training_routine(experience.observation[:, agent_i],
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experience.next_observation[:, agent_i],
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experience.action[:, agent_i].unsqueeze(-1))
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pred_q += q_values
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target_q_raw += next_q_values_raw
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target_q = experience.reward + (1 - experience.done) * self.gamma * target_q_raw
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#print(pred_q[0], target_q_raw[0], target_q[0], experience.reward[0])
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loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - target_q, 2))
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self._backprop_loss(loss)
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class MDQN(BaseQlearner):
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def __init__(self, *args, temperature=0.03, alpha=0.9, clip_l0=-1.0, **kwargs):
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super(MDQN, self).__init__(*args, **kwargs)
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assert self.n_agents == 1, 'M-DQN currently only supports single agent training'
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self.temperature = temperature
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self.alpha = alpha
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self.clip0 = clip_l0
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def tau_ln_pi(self, qs):
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# computes log(softmax(qs/temperature))
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# Custom log-sum-exp trick from page 18 to compute the log-policy terms
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v_k = qs.max(-1)[0].unsqueeze(-1)
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advantage = qs - v_k
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logsum = torch.logsumexp(advantage / self.temperature, -1).unsqueeze(-1)
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tau_ln_pi = advantage - self.temperature * logsum
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return tau_ln_pi
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def train(self):
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if len(self.buffer) < self.batch_size: return
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for _ in range(self.n_grad_steps):
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experience = self.buffer.sample(self.batch_size, cer=self.train_every_n_steps)
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q_target_next = self.target_q_net(experience.next_observation).detach()
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tau_log_pi_next = self.tau_ln_pi(q_target_next)
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q_k_targets = self.target_q_net(experience.observation).detach()
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log_pi = self.tau_ln_pi(q_k_targets)
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pi_target = F.softmax(q_target_next / self.temperature, dim=-1)
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q_target = (self.gamma * (pi_target * (q_target_next - tau_log_pi_next) * (1 - experience.done)).sum(-1)).unsqueeze(-1)
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munchausen_addon = log_pi.gather(-1, experience.action)
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munchausen_reward = (experience.reward + self.alpha * torch.clamp(munchausen_addon, min=self.clip0, max=0))
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# Compute Q targets for current states
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m_q_target = munchausen_reward + q_target
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# Get expected Q values from local model
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q_k = self.q_net(experience.observation)
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pred_q = q_k.gather(-1, experience.action)
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# Compute loss
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loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - m_q_target, 2))
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self._backprop_loss(loss)
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if __name__ == '__main__':
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from environments.factory.simple_factory import SimpleFactory, DirtProperties, MovementProperties
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N_AGENTS = 2
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dirt_props = DirtProperties(clean_amount=3, gain_amount=0.2, max_global_amount=30,
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max_local_amount=5, spawn_frequency=1, max_spawn_ratio=0.05)
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move_props = MovementProperties(allow_diagonal_movement=True,
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allow_square_movement=True,
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allow_no_op=False)
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env = SimpleFactory(dirt_properties=dirt_props, movement_properties=move_props, n_agents=N_AGENTS, pomdp_radius=2, max_steps=400, omit_agent_slice_in_obs=False, combin_agent_slices_in_obs=True)
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dqn, target_dqn = BaseDQN(), BaseDQN()
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learner = BaseQlearner(dqn, target_dqn, env, BaseBuffer(40000), target_update=3500, lr=0.0007, gamma=0.99, n_agents=N_AGENTS, tau=0.95, max_grad_norm=10,
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train_every_n_steps=4, eps_end=0.025, n_grad_steps=1, reg_weight=0.1, exploration_fraction=0.25, batch_size=64)
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learner.learn(100000)
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