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sac_cartpole.py
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sac_cartpole.py
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import random
import time
import gym
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
from copy import deepcopy
from collections import deque
class GumbelSoftmax(torch.distributions.RelaxedOneHotCategorical):
def sample(self, sample_shape=torch.Size()):
'''Gumbel-softmax sampling. Note rsample is inherited from RelaxedOneHotCategorical'''
u = torch.empty(self.logits.size(), device=self.logits.device, dtype=self.logits.dtype).uniform_(0, 1)
noisy_logits = self.logits - torch.log(-torch.log(u))
return torch.argmax(noisy_logits, dim=-1)
def log_prob(self, value):
'''value is one-hot or relaxed'''
if value.shape != self.logits.shape:
value = F.one_hot(value.long(), self.logits.shape[-1]).float()
assert value.shape == self.logits.shape
return - torch.sum(- value * F.log_softmax(self.logits, -1), -1)
class SAC(nn.Module):
def __init__(self,
state_dim: int,
action_dim: int,
pi_lr: float = 1e-3,
q_lr: float = 1e-3,
gamma: float = 0.99,
alpha: float = 1e-3,
tau: float = 1e-2,
capacity: int = 10_000
):
super(SAC, self).__init__()
self.pi_model = nn.Sequential(
nn.Linear(state_dim, 128),
nn.ReLU(),
nn.Linear(128, 128),
nn.ReLU(),
nn.Linear(128, action_dim),
)
self.q1_model = nn.Sequential(
nn.Linear(state_dim + action_dim, 128),
nn.ReLU(),
nn.Linear(128, 128),
nn.ReLU(),
nn.Linear(128, 1)
)
self.q2_model = nn.Sequential(
nn.Linear(state_dim + action_dim, 128),
nn.ReLU(),
nn.Linear(128, 128),
nn.ReLU(),
nn.Linear(128, 1)
)
self.q1_target_model = deepcopy(self.q1_model)
self.q2_target_model = deepcopy(self.q2_model)
self.state_dim = state_dim
self.action_dim = action_dim
self.gamma = gamma
self.alpha = alpha
self.tau = tau
self.memory = deque([], maxlen=capacity)
self.pi_optimizer = optim.Adam(self.pi_model.parameters(), lr=pi_lr)
self.q1_optimizer = optim.Adam(self.q1_model.parameters(), lr=q_lr)
self.q2_optimizer = optim.Adam(self.q2_model.parameters(), lr=q_lr)
def predict_action(self, states: torch.FloatTensor):
logits = self.pi_model(states)
dist = GumbelSoftmax(temperature=0.01, logits=logits)
actions = dist.rsample()
log_probs = dist.log_prob(actions)
return actions, log_probs
def get_action(self, state):
states = torch.FloatTensor(state).unsqueeze(0)
action, _ = self.predict_action(states)
return torch.argmax(action).squeeze(0).detach().numpy()
def update_model(self, loss, optimizer, model=None, target_model=None):
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (model is not None) and (target_model is not None):
for param, target_param in zip(model.parameters(), target_model.parameters()):
new_target_param = (1 - self.tau) * target_param + self.tau * param
target_param.data.copy_(new_target_param)
def fit(self, state, action, reward, done, next_state, batch_size: int = 64):
self.memory.append([state, action, reward, done, next_state])
if len(self.memory) > batch_size:
batch = random.sample(self.memory, batch_size)
states, actions, rewards, dones, next_states = map(np.array, zip(*batch))
states, actions, rewards, dones, next_states = map(torch.FloatTensor,
[states, actions, rewards, dones, next_states])
rewards, dones = rewards.unsqueeze(1), dones.unsqueeze(1)
next_actions, next_log_probs = self.predict_action(next_states)
next_states_and_actions = torch.concat([next_states, next_actions], dim=1)
next_q1_values = self.q1_target_model(next_states_and_actions)
next_q2_values = self.q1_target_model(next_states_and_actions)
next_q_values = torch.min(next_q1_values, next_q2_values)
targets = rewards + self.gamma * (1 - dones) * (next_q_values - self.alpha * next_log_probs)
actions = F.one_hot(actions.type(torch.LongTensor), self.action_dim)
states_and_actions = torch.concat([states, actions], dim=1)
q1_loss = torch.mean((targets.detach() - self.q1_model(states_and_actions)) ** 2)
q2_loss = torch.mean((targets.detach() - self.q2_model(states_and_actions)) ** 2)
self.update_model(q1_loss, self.q1_optimizer, self.q1_model, self.q1_target_model)
self.update_model(q2_loss, self.q2_optimizer, self.q2_model, self.q2_target_model)
pred_actions, pred_log_probs = self.predict_action(states)
states_and_pred_actions = torch.concat([states, pred_actions], dim=1)
pred_q1_values = self.q1_target_model(states_and_pred_actions)
pred_q2_values = self.q1_target_model(states_and_pred_actions)
pred_q_values = torch.min(pred_q1_values, pred_q2_values)
pi_loss = - torch.mean(pred_q_values - self.alpha * pred_log_probs)
self.update_model(pi_loss, self.pi_optimizer)
def visualize(env, agent, max_len=1000):
trajectory = {'states': [], 'actions': [], 'rewards': []}
obs = env.reset()
state = obs
for _ in range(max_len):
trajectory['states'].append(state)
action = agent.get_action(state)
trajectory['actions'].append(action)
obs, reward, done, _ = env.step(action)
trajectory['rewards'].append(reward)
state = obs
time.sleep(0.03)
env.render()
if done:
break
return trajectory
if __name__ == '__main__':
env = gym.make('CartPole-v1')
env.reset()
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
print(f"n_states: {state_dim}, n_actions: {action_dim}")
episode_n = 100
session_len = 500
total_rewards = []
agent = SAC(state_dim, action_dim)
for episode in range(episode_n):
state = env.reset()
total_reward = 0
for _ in range(session_len):
action = agent.get_action(state)
next_state, reward, done, _ = env.step(action)
agent.fit(state, action, reward, done, next_state)
total_reward += reward
state = next_state
total_rewards.append(total_reward)
print(f'episode {episode}, reward: {total_reward}')
visualize(env, agent)