from typing import NamedTuple, Union from collections import namedtuple, deque import numpy as np import random import torch import torch.nn as nn import torch.nn.functional as F from stable_baselines3.common.utils import polyak_update from stable_baselines3.common.buffers import ReplayBuffer import copy class Experience(NamedTuple): observation: np.ndarray next_observation: np.ndarray action: np.ndarray reward: Union[float, np.ndarray] done : Union[bool, np.ndarray] priority: np.ndarray = 1 class BaseBuffer: def __init__(self, size: int): self.size = size self.experience = deque(maxlen=size) def __len__(self): return len(self.experience) def add(self, experience): self.experience.append(experience) def sample(self, k): sample = random.choices(self.experience, k=k) observations = torch.stack([torch.from_numpy(e.observation) for e in sample], 0).float() next_observations = torch.stack([torch.from_numpy(e.next_observation) for e in sample], 0).float() actions = torch.tensor([e.action for e in sample]).long() rewards = torch.tensor([e.reward for e in sample]).float().view(-1, 1) dones = torch.tensor([e.done for e in sample]).float().view(-1, 1) return Experience(observations, next_observations, actions, rewards, dones) class PERBuffer(BaseBuffer): def __init__(self, size, alpha=0.2): super(PERBuffer, self).__init__(size) self.alpha = alpha def sample(self, k): pr = [abs(e.priority)**self.alpha for e in self.experience] pr = np.array(pr) / sum(pr) idxs = random.choices(range(len(self)), weights=pr, k=k) pass class BaseDQN(nn.Module): def __init__(self): super(BaseDQN, self).__init__() self.net = nn.Sequential( nn.Linear(3*5*5, 128), nn.ReLU(), nn.Linear(128, 128), nn.ReLU(), nn.Linear(128, 9) ) def act(self, x) -> np.ndarray: with torch.no_grad(): action = self.net(x.view(x.shape[0], -1)).max(-1)[1].numpy() return action def forward(self, x): return self.net(x.view(x.shape[0], -1)) def random_action(self): return random.randrange(0, 5) class BaseQlearner: def __init__(self, q_net, target_q_net, env, buffer, target_update, eps_end, n_agents=1, gamma=0.99, train_every_n_steps=4, n_grad_steps=1, exploration_fraction=0.2, batch_size=64, lr=1e-4, reg_weight=0.0): self.q_net = q_net self.target_q_net = target_q_net #self.q_net.apply(self.weights_init) polyak_update(self.q_net.parameters(), self.target_q_net.parameters(), 1) self.target_q_net.eval() self.env = env self.buffer = buffer self.target_update = target_update self.eps = 1. self.eps_end = eps_end self.exploration_fraction = exploration_fraction self.batch_size = batch_size self.gamma = gamma self.train_every_n_steps = train_every_n_steps self.n_grad_steps = n_grad_steps self.lr = lr self.reg_weight = reg_weight self.n_agents = n_agents self.device = 'cpu' self.optimizer = torch.optim.Adam(self.q_net.parameters(), lr=self.lr) self.running_reward = deque(maxlen=30) self.running_loss = deque(maxlen=30) def to(self, device): self.device = device return self @staticmethod def weights_init(module, activation='relu'): if isinstance(module, (nn.Linear, nn.Conv2d)): nn.init.xavier_normal_(module.weight, gain=torch.nn.init.calculate_gain(activation)) if module.bias is not None: module.bias.data.fill_(0.0) def anneal_eps(self, step, n_steps): fraction = min(float(step) / int(self.exploration_fraction*n_steps), 1.0) self.eps = 1 + fraction * (self.eps_end - 1) def get_action(self, obs) -> Union[int, np.ndarray]: o = torch.from_numpy(obs).unsqueeze(0) if self.n_agents <= 1 else torch.from_numpy(obs) if np.random.rand() > self.eps: action = self.q_net.act(o.float()) else: action = np.array([self.env.action_space.sample() for _ in range(self.n_agents)]) return action def learn(self, n_steps): step = 0 while step < n_steps: obs, done = self.env.reset(), False total_reward = 0 while not done: action = self.get_action(obs) next_obs, reward, done, info = self.env.step(action if not len(action) == 1 else action[0]) experience = Experience(observation=obs.copy(), next_observation=next_obs.copy(), action=action, reward=reward, done=done) # do we really need to copy? self.buffer.add(experience) # end of step routine obs = next_obs step += 1 total_reward += reward self.anneal_eps(step, n_steps) if step % self.train_every_n_steps == 0: self.train() if step % self.target_update == 0: print('UPDATE') polyak_update(self.q_net.parameters(), self.target_q_net.parameters(), 1.0) self.running_reward.append(total_reward) if step % 10 == 0: print(f'Step: {step} ({(step/n_steps)*100:.2f}%)\tRunning reward: {sum(list(self.running_reward))/len(self.running_reward):.2f}\t' f' eps: {self.eps:.4f}\tRunning loss: {sum(list(self.running_loss))/len(self.running_loss):.4f}') def _training_routine(self, obs, next_obs, action): current_q_values = self.q_net(obs) current_q_values = torch.gather(current_q_values, dim=1, index=action) next_q_values_raw = self.target_q_net(next_obs).max(dim=-1)[0].reshape(-1, 1).detach() return current_q_values, next_q_values_raw def train(self): if len(self.buffer) < self.batch_size: return for _ in range(self.n_grad_steps): experience = self.buffer.sample(self.batch_size) #print(experience.observation.shape, experience.next_observation.shape, experience.action.shape, experience.reward.shape, experience.done.shape) if self.n_agents <= 1: pred_q, target_q_raw = self._training_routine(experience.observation, experience.next_observation, experience.action) else: pred_q, target_q_raw = torch.zeros((self.batch_size, 1)), torch.zeros((self.batch_size, 1)) for agent_i in range(self.n_agents): q_values, next_q_values_raw = self._training_routine(experience.observation[:, agent_i], experience.next_observation[:, agent_i], experience.action[:, agent_i].unsqueeze(-1) ) pred_q += q_values target_q_raw += next_q_values_raw target_q = experience.reward + (1 - experience.done) * self.gamma * target_q_raw loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - target_q, 2)) print(target_q) # log loss self.running_loss.append(loss.item()) # Optimize the model self.optimizer.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(self.q_net.parameters(), 10) self.optimizer.step() if __name__ == '__main__': from environments.factory.simple_factory import SimpleFactory, DirtProperties, MovementProperties from algorithms.reg_dqn import RegDQN from stable_baselines3.common.vec_env import DummyVecEnv N_AGENTS = 1 dirt_props = DirtProperties(clean_amount=3, gain_amount=0.2, max_global_amount=30, max_local_amount=5, spawn_frequency=1, max_spawn_ratio=0.05) move_props = MovementProperties(allow_diagonal_movement=True, allow_square_movement=True, allow_no_op=False) 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) env = DummyVecEnv([lambda: env]) print(env) from stable_baselines3.dqn import DQN #dqn = RegDQN('MlpPolicy', env, verbose=True, buffer_size = 50000, learning_starts = 64, batch_size = 64, # target_update_interval = 5000, exploration_fraction = 0.25, exploration_final_eps = 0.025, # train_freq=4, gradient_steps=1, reg_weight=0.05) #dqn.learn(100000) print(env.observation_space, env.action_space) dqn, target_dqn = BaseDQN(), BaseDQN() learner = BaseQlearner(dqn, target_dqn, env, BaseBuffer(50000), target_update=10000, lr=0.0001, gamma=0.99, n_agents=N_AGENTS, train_every_n_steps=4, eps_end=0.05, n_grad_steps=1, reg_weight=0.05, exploration_fraction=0.25, batch_size=64) learner.learn(100000)