diff --git a/CustomPreprocessor.py b/RLLib_training/CustomPreprocessor.py
similarity index 90%
rename from CustomPreprocessor.py
rename to RLLib_training/CustomPreprocessor.py
index 3a2e5c106baad72a9b99226e6b0065797f2c0ddb..7d23b8cf8030ac12efb378d6dae54b90b1bdc678 100644
--- a/CustomPreprocessor.py
+++ b/RLLib_training/CustomPreprocessor.py
@@ -36,7 +36,7 @@ def norm_obs_clip(obs, clip_min=-1, clip_max=1):
: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
+ :return: returns normalized and clipped observation
"""
max_obs = max(1, max_lt(obs, 1000))
min_obs = max(0, min_lt(obs, 0))
@@ -53,7 +53,11 @@ class CustomPreprocessor(Preprocessor):
return (105,)
def transform(self, observation):
- return norm_obs_clip(observation) # return the preprocessed observation
+ if len(observation) == 105:
+ return norm_obs_clip(observation)
+ else:
+ return observation
+
diff --git a/README.md b/RLLib_training/README.md
similarity index 100%
rename from README.md
rename to RLLib_training/README.md
diff --git a/RailEnvRLLibWrapper.py b/RLLib_training/RailEnvRLLibWrapper.py
similarity index 100%
rename from RailEnvRLLibWrapper.py
rename to RLLib_training/RailEnvRLLibWrapper.py
diff --git a/experiment_configs/CustomModels.py b/RLLib_training/experiment_configs/CustomModels.py
similarity index 100%
rename from experiment_configs/CustomModels.py
rename to RLLib_training/experiment_configs/CustomModels.py
diff --git a/experiment_configs/entropy_coeff_benchmark/config.gin b/RLLib_training/experiment_configs/entropy_coeff_benchmark/config.gin
similarity index 100%
rename from experiment_configs/entropy_coeff_benchmark/config.gin
rename to RLLib_training/experiment_configs/entropy_coeff_benchmark/config.gin
diff --git a/experiment_configs/n_agents_experiment/config.gin b/RLLib_training/experiment_configs/n_agents_experiment/config.gin
similarity index 100%
rename from experiment_configs/n_agents_experiment/config.gin
rename to RLLib_training/experiment_configs/n_agents_experiment/config.gin
diff --git a/experiment_configs/observation_benchmark/config.gin b/RLLib_training/experiment_configs/observation_benchmark/config.gin
similarity index 100%
rename from experiment_configs/observation_benchmark/config.gin
rename to RLLib_training/experiment_configs/observation_benchmark/config.gin
diff --git a/train.py b/RLLib_training/train.py
similarity index 100%
rename from train.py
rename to RLLib_training/train.py
diff --git a/train_experiment.py b/RLLib_training/train_experiment.py
similarity index 100%
rename from train_experiment.py
rename to RLLib_training/train_experiment.py
diff --git a/torch_training/Nets/avoid_checkpoint15000.pth b/torch_training/Nets/avoid_checkpoint15000.pth
new file mode 100644
index 0000000000000000000000000000000000000000..14882a37a86085b137f4422b6bba75f387a2d3b5
Binary files /dev/null and b/torch_training/Nets/avoid_checkpoint15000.pth differ
diff --git a/torch_training/__init__.py b/torch_training/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/torch_training/dueling_double_dqn.py b/torch_training/dueling_double_dqn.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2ff1a7f1f8502926efa88c932c31bff1a2ed179
--- /dev/null
+++ b/torch_training/dueling_double_dqn.py
@@ -0,0 +1,200 @@
+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 baselines.torch_training.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/torch_training/model.py b/torch_training/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a5b3d613342a4fba8e2c8f1f45df21381e12684
--- /dev/null
+++ b/torch_training/model.py
@@ -0,0 +1,61 @@
+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/torch_training/training_navigation.py b/torch_training/training_navigation.py
new file mode 100644
index 0000000000000000000000000000000000000000..8736e3d8e665e521e2b34de9136488925782ee28
--- /dev/null
+++ b/torch_training/training_navigation.py
@@ -0,0 +1,214 @@
+import random
+from collections import deque
+
+import numpy as np
+import torch
+
+from baselines.torch_training.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('./baselines/torch_training/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