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Commit e47d3f5f authored by Egli Adrian (IT-SCI-API-PFI)'s avatar Egli Adrian (IT-SCI-API-PFI)
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Merge branch 'master' of ssh://gitlab.aicrowd.com/flatland/flatland

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baselines/Nets/avoid_checkpoint9900.pth deleted 100644 → 0
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import random
from collections import deque
import numpy as np
import torch
from flatland.baselines.dueling_double_dqn import Agent
from flatland.envs.generators import complex_rail_generator
from flatland.envs.rail_env import RailEnv
from flatland.utils.rendertools import RenderTool
random.seed(1)
np.random.seed(1)
# Example generate a rail given a manual specification,
# a map of tuples (cell_type, rotation)
transition_probability = [15, # empty cell - Case 0
5, # Case 1 - straight
5, # Case 2 - simple switch
1, # Case 3 - diamond crossing
1, # Case 4 - single slip
1, # Case 5 - double slip
1, # Case 6 - symmetrical
0, # Case 7 - dead end
1, # Case 1b (8) - simple turn right
1, # Case 1c (9) - simple turn left
1] # Case 2b (10) - simple switch mirrored
# Example generate a random rail
"""
env = RailEnv(width=20,
height=20,
rail_generator=random_rail_generator(cell_type_relative_proportion=transition_probability),
number_of_agents=1)
"""
env = RailEnv(width=15,
height=15,
rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=10, min_dist=10, max_dist=99999, seed=0),
number_of_agents=5)
"""
env = RailEnv(width=20,
height=20,
rail_generator=rail_from_list_of_saved_GridTransitionMap_generator(
['../notebooks/temp.npy']),
number_of_agents=3)
"""
env_renderer = RenderTool(env, gl="QTSVG")
handle = env.get_agent_handles()
state_size = 105 * 2
action_size = 4
n_trials = 15000
eps = 1.
eps_end = 0.005
eps_decay = 0.9995
action_dict = dict()
final_action_dict = dict()
scores_window = deque(maxlen=100)
done_window = deque(maxlen=100)
time_obs = deque(maxlen=2)
scores = []
dones_list = []
action_prob = [0] * 4
agent_obs = [None] * env.get_num_agents()
agent_next_obs = [None] * env.get_num_agents()
agent = Agent(state_size, action_size, "FC", 0)
agent.qnetwork_local.load_state_dict(torch.load('./flatland/baselines/Nets/avoid_checkpoint15000.pth'))
demo = True
def max_lt(seq, val):
"""
Return greatest item in seq for which item < val applies.
None is returned if seq was empty or all items in seq were >= val.
"""
max = 0
idx = len(seq) - 1
while idx >= 0:
if seq[idx] < val and seq[idx] >= 0 and seq[idx] > max:
max = seq[idx]
idx -= 1
return max
def min_lt(seq, val):
"""
Return smallest item in seq for which item > val applies.
None is returned if seq was empty or all items in seq were >= val.
"""
min = np.inf
idx = len(seq) - 1
while idx >= 0:
if seq[idx] > val and seq[idx] < min:
min = seq[idx]
idx -= 1
return min
def norm_obs_clip(obs, clip_min=-1, clip_max=1):
"""
This function returns the difference between min and max value of an observation
:param obs: Observation that should be normalized
:param clip_min: min value where observation will be clipped
:param clip_max: max value where observation will be clipped
:return: returnes normalized and clipped observatoin
"""
max_obs = max(1, max_lt(obs, 1000))
min_obs = max(0, min_lt(obs, 0))
if max_obs == min_obs:
return np.clip(np.array(obs) / max_obs, clip_min, clip_max)
norm = np.abs(max_obs - min_obs)
if norm == 0:
norm = 1.
return np.clip((np.array(obs) - min_obs) / norm, clip_min, clip_max)
for trials in range(1, n_trials + 1):
# Reset environment
obs = env.reset()
final_obs = obs.copy()
final_obs_next = obs.copy()
for a in range(env.get_num_agents()):
data, distance = env.obs_builder.split_tree(tree=np.array(obs[a]), num_features_per_node=5, current_depth=0)
data = norm_obs_clip(data)
distance = norm_obs_clip(distance)
obs[a] = np.concatenate((data, distance))
for i in range(2):
time_obs.append(obs)
# env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
for a in range(env.get_num_agents()):
agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
score = 0
env_done = 0
# Run episode
for step in range(100):
if demo:
env_renderer.renderEnv(show=True)
# print(step)
# Action
for a in range(env.get_num_agents()):
if demo:
eps = 0
# action = agent.act(np.array(obs[a]), eps=eps)
action = agent.act(agent_obs[a])
action_prob[action] += 1
action_dict.update({a: action})
# Environment step
next_obs, all_rewards, done, _ = env.step(action_dict)
for a in range(env.get_num_agents()):
data, distance = env.obs_builder.split_tree(tree=np.array(next_obs[a]), num_features_per_node=5,
current_depth=0)
data = norm_obs_clip(data)
distance = norm_obs_clip(distance)
next_obs[a] = np.concatenate((data, distance))
time_obs.append(next_obs)
# Update replay buffer and train agent
for a in range(env.get_num_agents()):
agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
if done[a]:
final_obs[a] = agent_obs[a].copy()
final_obs_next[a] = agent_next_obs[a].copy()
final_action_dict.update({a: action_dict[a]})
if not demo and not done[a]:
agent.step(agent_obs[a], action_dict[a], all_rewards[a], agent_next_obs[a], done[a])
score += all_rewards[a]
agent_obs = agent_next_obs.copy()
if done['__all__']:
env_done = 1
for a in range(env.get_num_agents()):
agent.step(final_obs[a], final_action_dict[a], all_rewards[a], final_obs_next[a], done[a])
break
# Epsilon decay
eps = max(eps_end, eps_decay * eps) # decrease epsilon
done_window.append(env_done)
scores_window.append(score) # save most recent score
scores.append(np.mean(scores_window))
dones_list.append((np.mean(done_window)))
print('\rTraining {} Agents.\t Episode {}\t Average Score: {:.0f}\tDones: {:.2f}%' +
'\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
env.get_num_agents(),
trials,
np.mean(scores_window),
100 * np.mean(done_window),
eps, action_prob / np.sum(action_prob)), end=" ")
if trials % 100 == 0:
print(
'\rTraining {} Agents.\t Episode {}\t Average Score: {:.0f}\tDones: {:.2f}%' +
'\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
env.get_num_agents(),
trials,
np.mean(scores_window),
100 * np.mean(done_window),
eps,
action_prob / np.sum(action_prob)))
torch.save(agent.qnetwork_local.state_dict(),
'../flatland/baselines/Nets/avoid_checkpoint' + str(trials) + '.pth')
action_prob = [1] * 4
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import copy
import os
import random
from collections import namedtuple, deque, Iterable
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from flatland.baselines.model import QNetwork, QNetwork2
BUFFER_SIZE = int(1e5) # replay buffer size
BATCH_SIZE = 512 # minibatch size
GAMMA = 0.99 # discount factor 0.99
TAU = 1e-3 # for soft update of target parameters
LR = 0.5e-4 # learning rate 5
UPDATE_EVERY = 10 # how often to update the network
double_dqn = True # If using double dqn algorithm
input_channels = 5 # Number of Input channels
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu")
print(device)
class Agent:
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, net_type, seed, double_dqn=True, input_channels=5):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.version = net_type
self.double_dqn = double_dqn
# Q-Network
if self.version == "Conv":
self.qnetwork_local = QNetwork2(state_size, action_size, seed, input_channels).to(device)
self.qnetwork_target = copy.deepcopy(self.qnetwork_local)
else:
self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = copy.deepcopy(self.qnetwork_local)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def save(self, filename):
torch.save(self.qnetwork_local.state_dict(), filename + ".local")
torch.save(self.qnetwork_target.state_dict(), filename + ".target")
def load(self, filename):
if os.path.exists(filename + ".local"):
self.qnetwork_local.load_state_dict(torch.load(filename + ".local"))
if os.path.exists(filename + ".target"):
self.qnetwork_target.load_state_dict(torch.load(filename + ".target"))
def step(self, state, action, reward, next_state, done, train=True):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
if train:
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
if self.double_dqn:
# Double DQN
q_best_action = self.qnetwork_local(next_states).max(1)[1]
Q_targets_next = self.qnetwork_target(next_states).gather(1, q_best_action.unsqueeze(-1))
else:
# DQN
Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(-1)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
class ReplayBuffer:
"""Fixed-size buffer to store experience tuples."""
def __init__(self, action_size, buffer_size, batch_size, seed):
"""Initialize a ReplayBuffer object.
Params
======
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
seed (int): random seed
"""
self.action_size = action_size
self.memory = deque(maxlen=buffer_size)
self.batch_size = batch_size
self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
self.seed = random.seed(seed)
def add(self, state, action, reward, next_state, done):
"""Add a new experience to memory."""
e = self.experience(np.expand_dims(state, 0), action, reward, np.expand_dims(next_state, 0), done)
self.memory.append(e)
def sample(self):
"""Randomly sample a batch of experiences from memory."""
experiences = random.sample(self.memory, k=self.batch_size)
states = torch.from_numpy(self.__v_stack_impr([e.state for e in experiences if e is not None])) \
.float().to(device)
actions = torch.from_numpy(self.__v_stack_impr([e.action for e in experiences if e is not None])) \
.long().to(device)
rewards = torch.from_numpy(self.__v_stack_impr([e.reward for e in experiences if e is not None])) \
.float().to(device)
next_states = torch.from_numpy(self.__v_stack_impr([e.next_state for e in experiences if e is not None])) \
.float().to(device)
dones = torch.from_numpy(self.__v_stack_impr([e.done for e in experiences if e is not None]).astype(np.uint8)) \
.float().to(device)
return (states, actions, rewards, next_states, dones)
def __len__(self):
"""Return the current size of internal memory."""
return len(self.memory)
def __v_stack_impr(self, states):
sub_dim = len(states[0][0]) if isinstance(states[0], Iterable) else 1
np_states = np.reshape(np.array(states), (len(states), sub_dim))
return np_states
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import torch.nn as nn
import torch.nn.functional as F
class QNetwork(nn.Module):
def __init__(self, state_size, action_size, seed, hidsize1=128, hidsize2=128):
super(QNetwork, self).__init__()
self.fc1_val = nn.Linear(state_size, hidsize1)
self.fc2_val = nn.Linear(hidsize1, hidsize2)
self.fc3_val = nn.Linear(hidsize2, 1)
self.fc1_adv = nn.Linear(state_size, hidsize1)
self.fc2_adv = nn.Linear(hidsize1, hidsize2)
self.fc3_adv = nn.Linear(hidsize2, action_size)
def forward(self, x):
val = F.relu(self.fc1_val(x))
val = F.relu(self.fc2_val(val))
val = self.fc3_val(val)
# advantage calculation
adv = F.relu(self.fc1_adv(x))
adv = F.relu(self.fc2_adv(adv))
adv = self.fc3_adv(adv)
return val + adv - adv.mean()
class QNetwork2(nn.Module):
def __init__(self, state_size, action_size, seed, input_channels, hidsize1=128, hidsize2=64):
super(QNetwork2, self).__init__()
self.conv1 = nn.Conv2d(input_channels, 16, kernel_size=3, stride=1)
self.bn1 = nn.BatchNorm2d(16)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=3)
self.bn2 = nn.BatchNorm2d(32)
self.conv3 = nn.Conv2d(32, 64, kernel_size=5, stride=3)
self.bn3 = nn.BatchNorm2d(64)
self.fc1_val = nn.Linear(6400, hidsize1)
self.fc2_val = nn.Linear(hidsize1, hidsize2)
self.fc3_val = nn.Linear(hidsize2, 1)
self.fc1_adv = nn.Linear(6400, hidsize1)
self.fc2_adv = nn.Linear(hidsize1, hidsize2)
self.fc3_adv = nn.Linear(hidsize2, action_size)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
# value function approximation
val = F.relu(self.fc1_val(x.view(x.size(0), -1)))
val = F.relu(self.fc2_val(val))
val = self.fc3_val(val)
# advantage calculation
adv = F.relu(self.fc1_adv(x.view(x.size(0), -1)))
adv = F.relu(self.fc2_adv(adv))
adv = self.fc3_adv(adv)
return val + adv - adv.mean()
flatland/envs/observations.py
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...@@ -479,11 +479,15 @@ class GlobalObsForRailEnv(ObservationBuilder): ...@@ -479,11 +479,15 @@ class GlobalObsForRailEnv(ObservationBuilder):
- transition map array with dimensions (env.height, env.width, 16), - transition map array with dimensions (env.height, env.width, 16),
assuming 16 bits encoding of transitions. assuming 16 bits encoding of transitions.
- Four 2D arrays containing respectively the position of the given agent, - Three 2D arrays (map_height, map_width, 3) containing respectively the position of the given agent,
the position of its target, the positions of the other agents and of the position of its target and the positions of the other agents targets.
their target.
- A 3D array (map_height, map_width, 4) containing the one hot encoding of directions
of the other agents at their position coordinates.
- A 4 elements array with one of encoding of the direction of the agent of interest.
- A 4 elements array with one of encoding of the direction.
""" """
def __init__(self): def __init__(self):
...@@ -504,21 +508,22 @@ class GlobalObsForRailEnv(ObservationBuilder): ...@@ -504,21 +508,22 @@ class GlobalObsForRailEnv(ObservationBuilder):
# self.targets[target_pos] += 1 # self.targets[target_pos] += 1
def get(self, handle): def get(self, handle):
obs = np.zeros((4, self.env.height, self.env.width)) obs_map_state = np.zeros((self.env.height, self.env.width, 3))
obs_other_agents_state = np.zeros((self.env.height, self.env.width, 4))
agents = self.env.agents agents = self.env.agents
agent = agents[handle] agent = agents[handle]
agent_pos = agents[handle].position agent_pos = agents[handle].position
obs[0][agent_pos] += 1 obs_map_state[agent_pos][0] += 1
obs[1][agent.target] += 1 obs_map_state[agent.target][1] += 1
for i in range(len(agents)): for i in range(len(agents)):
if i != handle: # TODO: handle used as index...? if i != handle: # TODO: handle used as index...?
agent2 = agents[i] agent2 = agents[i]
obs[3][agent2.position] += 1 obs_other_agents_state[agent2.position][agent2.direction] = 1
obs[2][agent2.target] += 1 obs_map_state[agent2.target][2] += 1
direction = np.zeros(4) direction = np.zeros(4)
direction[agent.direction] = 1 direction[agent.direction] = 1
return self.rail_obs, obs, direction return self.rail_obs, obs_map_state, obs_other_agents_state, direction
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