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rl_utils.py
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from tqdm import tqdm
import numpy as np
import torch
import collections
import random
class ReplayBuffer:
def __init__(self, capacity):
self.buffer = collections.deque(maxlen=capacity)
def add(self, state, action, reward, next_state, done):
self.buffer.append((state, action, reward, next_state, done))
def sample(self, batch_size):
transitions = random.sample(self.buffer, batch_size)
state, action, reward, next_state, done = zip(*transitions)
return np.array(state), action, reward, np.array(next_state), done
def size(self):
return len(self.buffer)
def moving_average(a, window_size):
cumulative_sum = np.cumsum(np.insert(a, 0, 0))
middle = (cumulative_sum[window_size:] - cumulative_sum[:-window_size]) / window_size
r = np.arange(1, window_size-1, 2)
begin = np.cumsum(a[:window_size-1])[::2] / r
end = (np.cumsum(a[:-window_size:-1])[::2] / r)[::-1]
return np.concatenate((begin, middle, end))
def train_on_policy_agent(env, agent, num_episodes):
return_list = []
for i in range(10):
with tqdm(total=int(num_episodes/10), desc='Iteration %d' % i) as pbar:
for i_episode in range(int(num_episodes/10)):
episode_return = 0
transition_dict = {'states': [], 'actions': [], 'next_states': [], 'rewards': [], 'dones': []}
state = env.reset()
done = False
while not done:
action = agent.take_action(state)
next_state, reward, done, _ = env.step(action)
transition_dict['states'].append(state)
transition_dict['actions'].append(action)
transition_dict['next_states'].append(next_state)
transition_dict['rewards'].append(reward)
transition_dict['dones'].append(done)
state = next_state
episode_return += reward
return_list.append(episode_return)
agent.update(transition_dict)
if (i_episode+1) % 10 == 0:
pbar.set_postfix({'episode': '%d' % (num_episodes/10 * i + i_episode+1), 'return': '%.3f' % np.mean(return_list[-10:])})
pbar.update(1)
return return_list
def train_off_policy_agent(env, agent, num_episodes, replay_buffer, minimal_size, batch_size):
return_list = []
for i in range(10):
with tqdm(total=int(num_episodes/10), desc='Iteration %d' % i) as pbar:
for i_episode in range(int(num_episodes/10)):
episode_return = 0
state = env.reset()
done = False
while not done:
action = agent.take_action(state)
next_state, reward, done, _ = env.step(action)
replay_buffer.add(state, action, reward, next_state, done)
state = next_state
episode_return += reward
if replay_buffer.size() > minimal_size:
b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(batch_size)
transition_dict = {'states': b_s, 'actions': b_a, 'next_states': b_ns, 'rewards': b_r, 'dones': b_d}
agent.update(transition_dict)
return_list.append(episode_return)
if (i_episode+1) % 10 == 0:
pbar.set_postfix({'episode': '%d' % (num_episodes/10 * i + i_episode+1), 'return': '%.3f' % np.mean(return_list[-10:])})
pbar.update(1)
return return_list
def compute_advantage(gamma, lmbda, td_delta):
td_delta = td_delta.detach().numpy()
advantage_list = []
advantage = 0.0
for delta in td_delta[::-1]:
advantage = gamma * lmbda * advantage + delta
advantage_list.append(advantage)
advantage_list.reverse()
return torch.tensor(advantage_list, dtype=torch.float)
from tqdm import tqdm
import numpy as np
import torch
import collections
import random
class ReplayBuffer:
def __init__(self, capacity):
self.buffer = collections.deque(maxlen=capacity)
def add(self, state, action, reward, next_state, done):
self.buffer.append((state, action, reward, next_state, done))
def sample(self, batch_size):
transitions = random.sample(self.buffer, batch_size)
state, action, reward, next_state, done = zip(*transitions)
return np.array(state), action, reward, np.array(next_state), done
def size(self):
return len(self.buffer)
def moving_average(a, window_size):
cumulative_sum = np.cumsum(np.insert(a, 0, 0))
middle = (cumulative_sum[window_size:] - cumulative_sum[:-window_size]) / window_size
r = np.arange(1, window_size-1, 2)
begin = np.cumsum(a[:window_size-1])[::2] / r
end = (np.cumsum(a[:-window_size:-1])[::2] / r)[::-1]
return np.concatenate((begin, middle, end))
def train_on_policy_agent(env, agent, num_episodes):
return_list = []
for i in range(10):
with tqdm(total=int(num_episodes/10), desc='Iteration %d' % i) as pbar:
for i_episode in range(int(num_episodes/10)):
episode_return = 0
transition_dict = {'states': [], 'actions': [], 'next_states': [], 'rewards': [], 'dones': []}
state = env.reset()
done = False
while not done:
action = agent.take_action(state)
next_state, reward, done, _ = env.step(action)
transition_dict['states'].append(state)
transition_dict['actions'].append(action)
transition_dict['next_states'].append(next_state)
transition_dict['rewards'].append(reward)
transition_dict['dones'].append(done)
state = next_state
episode_return += reward
return_list.append(episode_return)
agent.update(transition_dict)
if (i_episode+1) % 10 == 0:
pbar.set_postfix({'episode': '%d' % (num_episodes/10 * i + i_episode+1), 'return': '%.3f' % np.mean(return_list[-10:])})
pbar.update(1)
return return_list
def train_off_policy_agent(env, agent, num_episodes, replay_buffer, minimal_size, batch_size):
return_list = []
for i in range(10):
with tqdm(total=int(num_episodes/10), desc='Iteration %d' % i) as pbar:
for i_episode in range(int(num_episodes/10)):
episode_return = 0
state = env.reset()
done = False
while not done:
action = agent.take_action(state)
next_state, reward, done, _ = env.step(action)
replay_buffer.add(state, action, reward, next_state, done)
state = next_state
episode_return += reward
if replay_buffer.size() > minimal_size:
b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(batch_size)
transition_dict = {'states': b_s, 'actions': b_a, 'next_states': b_ns, 'rewards': b_r, 'dones': b_d}
agent.update(transition_dict)
return_list.append(episode_return)
if (i_episode+1) % 10 == 0:
pbar.set_postfix({'episode': '%d' % (num_episodes/10 * i + i_episode+1), 'return': '%.3f' % np.mean(return_list[-10:])})
pbar.update(1)
return return_list
def compute_advantage(gamma, lmbda, td_delta):
td_delta = td_delta.detach().numpy()
advantage_list = []
advantage = 0.0
for delta in td_delta[::-1]:
advantage = gamma * lmbda * advantage + delta
advantage_list.append(advantage)
advantage_list.reverse()
return torch.tensor(advantage_list, dtype=torch.float)
from tqdm import tqdm
import numpy as np
import torch
import collections
import random
class ReplayBuffer:
def __init__(self, capacity):
self.buffer = collections.deque(maxlen=capacity)
def add(self, state, action, reward, next_state, done):
self.buffer.append((state, action, reward, next_state, done))
def sample(self, batch_size):
transitions = random.sample(self.buffer, batch_size)
state, action, reward, next_state, done = zip(*transitions)
return np.array(state), action, reward, np.array(next_state), done
def size(self):
return len(self.buffer)
def moving_average(a, window_size):
cumulative_sum = np.cumsum(np.insert(a, 0, 0))
middle = (cumulative_sum[window_size:] - cumulative_sum[:-window_size]) / window_size
r = np.arange(1, window_size-1, 2)
begin = np.cumsum(a[:window_size-1])[::2] / r
end = (np.cumsum(a[:-window_size:-1])[::2] / r)[::-1]
return np.concatenate((begin, middle, end))
def train_on_policy_agent(env, agent, num_episodes):
return_list = []
for i in range(10):
with tqdm(total=int(num_episodes/10), desc='Iteration %d' % i) as pbar:
for i_episode in range(int(num_episodes/10)):
episode_return = 0
transition_dict = {'states': [], 'actions': [], 'next_states': [], 'rewards': [], 'dones': []}
state = env.reset()
done = False
while not done:
action = agent.take_action(state)
next_state, reward, done, _ = env.step(action)
transition_dict['states'].append(state)
transition_dict['actions'].append(action)
transition_dict['next_states'].append(next_state)
transition_dict['rewards'].append(reward)
transition_dict['dones'].append(done)
state = next_state
episode_return += reward
return_list.append(episode_return)
agent.update(transition_dict)
if (i_episode+1) % 10 == 0:
pbar.set_postfix({'episode': '%d' % (num_episodes/10 * i + i_episode+1), 'return': '%.3f' % np.mean(return_list[-10:])})
pbar.update(1)
return return_list
def train_off_policy_agent(env, agent, num_episodes, replay_buffer, minimal_size, batch_size):
return_list = []
for i in range(10):
with tqdm(total=int(num_episodes/10), desc='Iteration %d' % i) as pbar:
for i_episode in range(int(num_episodes/10)):
episode_return = 0
state = env.reset()
done = False
while not done:
action = agent.take_action(state)
next_state, reward, done, _ = env.step(action)
replay_buffer.add(state, action, reward, next_state, done)
state = next_state
episode_return += reward
if replay_buffer.size() > minimal_size:
b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(batch_size)
transition_dict = {'states': b_s, 'actions': b_a, 'next_states': b_ns, 'rewards': b_r, 'dones': b_d}
agent.update(transition_dict)
return_list.append(episode_return)
if (i_episode+1) % 10 == 0:
pbar.set_postfix({'episode': '%d' % (num_episodes/10 * i + i_episode+1), 'return': '%.3f' % np.mean(return_list[-10:])})
pbar.update(1)
return return_list
def compute_advantage(gamma, lmbda, td_delta):
td_delta = td_delta.detach().numpy()
advantage_list = []
advantage = 0.0
for delta in td_delta[::-1]:
advantage = gamma * lmbda * advantage + delta
advantage_list.append(advantage)
advantage_list.reverse()
return torch.tensor(advantage_list, dtype=torch.float)