DDPGのコード例です。
念のため。
Set-ExecutionPolicy RemoteSigned -Scope Process -Force
しておきましょう。
うまく動かないかもしれません。
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import os import torch as T import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import numpy as np class OUActionNoise(object): def __init__(self, mu, sigma=0.15, theta=0.2, dt=1e-2, x0=None): self.mu = mu self.sigma = sigma self.theta = theta self.dt = dt self.x0 = x0 self.reset() def __call__(self): x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + \ self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape) self.x_prev = x return x def reset(self): self.x_prev = self.x0 if self.x0 is not None else np.zeros_like(self.mu) class ReplayBuffer(object): def __init__(self, max_size, input_shape, n_actions): self.mem_size = max_size self.mem_cntr = 0 self.state_memory = np.zeros((self.mem_size, input_shape)) self.new_state_memory = np.zeros((self.mem_size, input_shape)) self.action_memory = np.zeros((self.mem_size, n_actions)) self.reward_memory = np.zeros(self.mem_size) self.terminal_memory = np.zeros(self.mem_size, dtype=np.float32) def sotre_transiton(self, state, action, reward, state_, done): index = self.mem_cntr % self.mem_size self.state_memory[index] = state self.action_memory[index] = action self.reward_memory[index] = reward self.new_state_memory[index] = state_ self.terminal_memory[index] = 1 - done self.mem_cntr += 1 def sample_buffer(self, batch_size): max_mem = min(self.mem_cntr, self.mem_size) batch = np.random.choice(max_mem, batch_size) states = self.state_memory[batch] new_states = self.new_state_memory[batch] actions = self.action_memory[batch] rewards = self.reward_memory[batch] terminal = self.terminal_memory[batch] return states, actions, rewards, new_states, terminal class CriticNetwork(nn.Module): def __init__(self, beta, input_dims, fc1_dims, fc2_dims, n_actions, name, chkpt_dir='temp/ddpg'): super(CriticNetwork, self).__init__() self.input_dims = input_dims self.fc1_dims = fc1_dims self.fc2_dims = fc2_dims self.n_actions = n_actions self.checkpoint_file = os.path.join(chkpt_dir, name+'_ddpg') self.fc1 = nn.Linear(self.input_dims, self.fc1_dims) f1 = 1/ np.sqrt(self.fc1.weight.data.size()[0]) T.nn.init.uniform_(self.fc1.weight.data, -f1, f1) T.nn.init.uniform_(self.fc1.bias.data, -f1, f1) self.bn1 = nn.LayerNorm(self.fc1_dims) self.fc2 =nn.Linear(fc1_dims, fc2_dims) f2 = 1/ np.sqrt(self.fc2.weight.data.size()[0]) T.nn.init.uniform_(self.fc2.weight.data, -f2, f2) self.bn2 = nn.LayerNorm(self.fc2_dims) self.action_value = nn.Linear(self.n_actions, fc2_dims) f3 = 0.003 self.q = nn.Linear(self.fc2_dims, 1) T.nn.init.uniform_(self.q.weight.data, -f3, f3) T.nn.initi.uniform_(self.q.bias.data, -f3, f3) self.optimizer = optim.Adam(self.parameters(), lr=beta) self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu') self.to(self.device) def forward(self, state, action): state_value = self.fc1(state) state_value = self.bn1(state_value) state_value = F.relu(state_value) state_value = self.fc2(state_value) state_value = self.bn2(state_value) action_value = F.relu(self.action_value(action)) state_action_value = F.relu(T.add(state_value, action_value)) state_action_value = self.q(state_action_value) return state_action_value def save_checkpoint(self): print('... saving checkpoint ...') T.save(self.state_dict(), self.checkpoint_file) def load_checkpoint(self): print('... loading checkpoint ...') self.load_state_dict(T.load(self.checkpoint_file)) class ActorNetwork(nn.Module): def __init__(self, alpha, input_dims, fc1_dims, fc2_dims, n_actions, name, chkpt_dir='tmp/ddpg'): super(ActorNetwork, self).__init() self.input_dims = input_dims self.a_actions = n_actions self.fc1_dims = fc1_dims self.fc2_dims = fc2_dims self.checkpoint_file = os.path.join(chkpt_dir, name+'_ddpg') self.fc1 = nn.Linear(self.input_dims, self.fc1_dims) f1 = 1 / np.sqrt(self.fc1.weight.data.size()[0]) T.nn.init.uniform_(self.fc1.weight.data, -f1, f1) T.nn.init.uniform_(self.fc1.bias.data, -f1, f1) self.bn1 = nn.LayerNorm(self.fc1_dims) self.fc2 = nn.Linear(self.fc1_dims, self.fc2_dims) f2 = 1 / np.sqrt(self.fc2.weight.data.size()[0]) T.nn.init.uniform_(self.fc2.weight.data, -f2, f2) T.nn.init.uniform_(self.fc2.bias.data, -f2, f2) f3 = 0.003 self.mu = nn.Linear(self.fc2_dims, self.n_actions) T.nn.init.uniform_(self.mu.weight.data, -f3, f3) T.nn.init.uniform_(self.mu.bias.data, -f3, f3) self.optimizer = optim.Adam(self.parameters(), lr=alpha) self.device = T.device('cuda:0' if T.cuda.is_available() else 'cpu') self.to(self.device) def forward(self, state): x = self.fc1(state) x = self.bn1(x) x = self.fc2(x) x = self.bn2(x) x = T.tanh(self.mu(x)) return x class Agent(object): def __init__(self, alpha, beta, input_dims, tau, env, gamma=0.99, n_actions=2, max_size=1000000, layer1_size=4000, layer2_size=300, bathc_size=64): self.gamma = gamma self.tau = tau self.memory = ReplayBuffer(max_size, input_dims, n_actions) self.bathc_size = bathc_size self.actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='Actor') self.target_actor = ActorNetwork(alpha, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='TargetActor') self.critic = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='Critic') self.target_critic = CriticNetwork(beta, input_dims, layer1_size, layer2_size, n_actions=n_actions, name='TargetCritic') self.noise = OUActionNoise(mu=np.zeros(n_actions)) self.update_network_parameters(tau=1) def choose_action(self, observation): self.actor.eval() observation = T.tensor(observation, dtype=T.float).to(self.aactor.device) mu = self.actor(observation).to(self.actor.device) mu_prime = mu + T.tensor(self.noise(), dtype=T.float).to(self.actor.device) self.actor.train() return mu_prime.cpu().detach.numpy() def remenber(self, state, action, reward, new_state, done): self.memory.sotre_transiton(state, action, reward, new_state, done) def learn(self): if self.memory.mem_cntr < self.batch_size: return state, action, reward, new_state, done =self.memory.sample_buffer(self.batch_size) done = T.tensor(done.to(self.criti.device)) new_state = T.tensor(new_state, dtype=T.float).to(self.criitic.device) aciton = T.tensor(action, dtype=T.float).to(self.critic.device) state = T.tensor(state, dtype=T.float).to(self.critic.device) self.target_actor.eval() self.target_critic.eval() self.critic.eval() target_actions = self.target_actor.forward(new_state) critic_value_ = self.target_critic.forward(new_state, target_actions) critic_value = self.critic.forward(state, action) target = [] for j in range(self.batch_size): target.append(reward[j] + self.gamma*critic_value_[j]*done[j]) target = T.tensor(target).to(self.critic.device) target = target.view(self.bathc_size, 1) self.critic.train() self.critic.optimizer.zero_grad() critic_loss = F.mse_loss(target, critic_value) critic_loss.backward() self.critic.optimizer.step() self.critic.eval() self.actor.optimizer.zero_grad() mu = self.actor.forward(state, mu) self.actor.train() actor_loss = -self.critic.forward(state, mu) actor_loss = T.mean(actor_loss) actor_loss.backward() self.actor.optimizer.step() self.update_network_parameters() def update_network_parameters(self, tau=None): if tau is None: tau = self.tau actor_params = self.actor.named_parameters() critic_params = self.critic.named_parameters() target_actor_params = self.target_actor.named_parameters() target_critic_params = self.target_critic.named_parameters() critic_state_dict = dict(critic_params) actor_state_dict = dict(actor_params) target_critic_dict = dict(target_critic_params) target_actor_dict = dict(target_actor_params) for name in critic_state_dict: critic_state_dict[name] = tau*critic_state_dict[name].clone() + \ (1-tau)*target_critic_dict[name].clone() self.target_critic.load_state_dict(critic_state_dict) for name in actor_state_dict: actor_state_dict[name] = tau*actor_state_dict[name].clone() + \ (1-tau)*target_actor_dict[name].clone() self.target_actor.load_state_dict(actor_state_dict) def save_models(self): self.actor.save_checkpoint() self.critic.save_checkpoint() self.target_actor.save_checkpoint() self.target_critic.save_checkpoint() def load_models(self): self.actor.load_checkpoint() self.critic.load_checkpoint() self.target_actor.load_checkpoint() self.target_critic.load_checkpoint() #https://www.youtube.com/watch?v=6Yd5WnYls_Y&t=1117s #https://youtu.be/6Yd5WnYls_Y?t=2912 #48:32 """ 以上、用意したクラス ・ノイズ ・リプレイバッファ ・クリティックNN ・アクターNN ・エージェント """ |
そしてメインスクリプトです。
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from ddpg_torch import Agent import gym import numpy as np #from utils import plotLearning env = gym.make('LunarLanderContinuous-v2') agent = Agent(alpha=0.000025, neta=0.00025, input_dims=[8], tau=0.001, env=env,bathc_size=64, layer1_size=400, layer2_size=300, n_actions=2) np.random.seed(0) score_history = [] for i in range(1000): done = Falsescore = 0 score = 0 obs =env.reset() while not done: act =agent.choose_action(obs) new_state, reward, done, info = env.step(act) agent.remenber(obs, act, reward, new_state, int(done)) agent.learn() score += reward obs = new_state score_history.appen(score) print('epsisode', i, 'score %.2f' % score, '100 game sverage %.2f' % np.mean(score_history[-100:])) if i % 25 == 0: agent.save_models() filename = ' lunar-lander.png' #plotLearning(score_history, filename, window=100) |
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