diff --git a/baselines/Nets/avoid_checkpoint9900.pth b/baselines/Nets/avoid_checkpoint9900.pth
deleted file mode 100644
index 5fae4c2908a10725315f388f7862860cd3ab5a52..0000000000000000000000000000000000000000
Binary files a/baselines/Nets/avoid_checkpoint9900.pth and /dev/null differ
diff --git a/baselines/Nets/dummy b/baselines/Nets/dummy
deleted file mode 100644
index bf453594ba586f318379101f00d73ec29f688e97..0000000000000000000000000000000000000000
--- a/baselines/Nets/dummy
+++ /dev/null
@@ -1 +0,0 @@
-dummy file for empty folder
diff --git a/examples/training_navigation.py b/examples/training_navigation.py
deleted file mode 100644
index 0cb9d275eda2a01932c4f632c1abd4fb662f4037..0000000000000000000000000000000000000000
--- a/examples/training_navigation.py
+++ /dev/null
@@ -1,214 +0,0 @@
-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
diff --git a/flatland/baselines/Nets/avoid_checkpoint15000.pth b/flatland/baselines/Nets/avoid_checkpoint15000.pth
deleted file mode 100644
index 14882a37a86085b137f4422b6bba75f387a2d3b5..0000000000000000000000000000000000000000
Binary files a/flatland/baselines/Nets/avoid_checkpoint15000.pth and /dev/null differ
diff --git a/flatland/baselines/__init__.py b/flatland/baselines/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/flatland/baselines/dueling_double_dqn.py b/flatland/baselines/dueling_double_dqn.py
deleted file mode 100644
index 41a27bf8431df7812f1b4f63e797aa426c17edf1..0000000000000000000000000000000000000000
--- a/flatland/baselines/dueling_double_dqn.py
+++ /dev/null
@@ -1,200 +0,0 @@
-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
diff --git a/flatland/baselines/model.py b/flatland/baselines/model.py
deleted file mode 100644
index 7a5b3d613342a4fba8e2c8f1f45df21381e12684..0000000000000000000000000000000000000000
--- a/flatland/baselines/model.py
+++ /dev/null
@@ -1,61 +0,0 @@
-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()
diff --git a/flatland/envs/observations.py b/flatland/envs/observations.py
index 21f9cb45900137e2167ab5811630bd05489d8d3a..3e02050eb4f01889f457a72929578a8eb5c36f29 100644
--- a/flatland/envs/observations.py
+++ b/flatland/envs/observations.py
@@ -204,7 +204,7 @@ class TreeObsForRailEnv(ObservationBuilder):
num_transitions = np.count_nonzero(possible_transitions)
# Root node - current position
# observation = [0, 0, 0, 0, self.distance_map[handle, position[0], position[1], orientation]]
- observation = [0, 0, 0, 0, self.distance_map[(handle, *agent.position,direc agent.direction)]]
+ observation = [0, 0, 0, 0, self.distance_map[(handle, *agent.position, agent.direction)]]
root_observation = observation[:]
visited = set()
# Start from the current orientation, and see which transitions are available;