diff --git a/RLLib_training/RailEnvRLLibWrapper.py b/RLLib_training/RailEnvRLLibWrapper.py
index 5ab92a48cf815c1cca17eac993c8ff528aabd32c..5642520a0e3649878789c093b07b3f0f06fb3f32 100644
--- a/RLLib_training/RailEnvRLLibWrapper.py
+++ b/RLLib_training/RailEnvRLLibWrapper.py
@@ -3,6 +3,7 @@ from ray.rllib.env.multi_agent_env import MultiAgentEnv
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.generators import random_rail_generator
from ray.rllib.utils.seed import seed as set_seed
+from flatland.envs.generators import complex_rail_generator, random_rail_generator
import numpy as np
@@ -19,14 +20,18 @@ class RailEnvRLLibWrapper(MultiAgentEnv):
vector_index = config.vector_index
else:
vector_index = 1
- #self.rail_generator = config["rail_generator"](nr_start_goal=config['number_of_agents'], min_dist=5,
- # nr_extra=30, seed=config['seed'] * (1+vector_index))
+
+ if config['rail_generator'] == "complex_rail_generator":
+ self.rail_generator = complex_rail_generator(nr_start_goal=config['number_of_agents'], min_dist=5,
+ nr_extra=config['nr_extra'], seed=config['seed'] * (1+vector_index))
+ else:
+ self.rail_generator = random_rail_generator()
+
set_seed(config['seed'] * (1+vector_index))
- #self.env = RailEnv(width=config["width"], height=config["height"],
- self.env = RailEnv(width=10, height=20,
+ self.env = RailEnv(width=config["width"], height=config["height"],
number_of_agents=config["number_of_agents"], obs_builder_object=config['obs_builder'])
- self.env.load('/mount/SDC/flatland/baselines/torch_training/railway/complex_scene.pkl')
+ # self.env.load('/home/guillaume/EPFL/Master_Thesis/flatland/baselines/torch_training/railway/complex_scene.pkl')
self.width = self.env.width
self.height = self.env.height
@@ -35,7 +40,7 @@ class RailEnvRLLibWrapper(MultiAgentEnv):
def reset(self):
self.agents_done = []
- obs = self.env.reset(False, False)
+ obs = self.env.reset()
o = dict()
# o['agents'] = obs
# obs[0] = [obs[0], np.ones((17, 17)) * 17]
diff --git a/RLLib_training/custom_preprocessors.py b/RLLib_training/custom_preprocessors.py
index 1c3fa0898582a6f9d093dbcac787d70805b2e0b6..9b7243f38a8b4cd06d7360db4a95c6099e80f7bc 100644
--- a/RLLib_training/custom_preprocessors.py
+++ b/RLLib_training/custom_preprocessors.py
@@ -1,7 +1,6 @@
import numpy as np
from ray.rllib.models.preprocessors import Preprocessor
-
def max_lt(seq, val):
"""
Return greatest item in seq for which item < val applies.
@@ -36,27 +35,27 @@ 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: returns normalized and clipped observation
+ :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)
+ 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)
+ return np.clip((np.array(obs) - min_obs) / norm, clip_min, clip_max)
class CustomPreprocessor(Preprocessor):
def _init_shape(self, obs_space, options):
- return (111,)
+ return obs_space.shape
def transform(self, observation):
- if len(observation) == 111:
- return norm_obs_clip(observation)
- else:
- return observation
+ # if len(observation) == 111:
+ return norm_obs_clip(observation)
+ # else:
+ # return observation
class ConvModelPreprocessor(Preprocessor):
diff --git a/RLLib_training/experiment_configs/env_complexity_benchmark/config.gin b/RLLib_training/experiment_configs/env_complexity_benchmark/config.gin
new file mode 100644
index 0000000000000000000000000000000000000000..82305a640693dbfa7efd946e3eb671727e0f72a5
--- /dev/null
+++ b/RLLib_training/experiment_configs/env_complexity_benchmark/config.gin
@@ -0,0 +1,25 @@
+run_experiment.name = "observation_benchmark_results"
+run_experiment.num_iterations = 1002
+run_experiment.save_every = 50
+run_experiment.hidden_sizes = [32, 32]
+
+run_experiment.map_width = 20
+run_experiment.map_height = 10
+run_experiment.n_agents = 8
+run_experiment.rail_generator = "complex_rail_generator"
+run_experiment.nr_extra = {"grid_search": [10, 20, 30, 40]}
+run_experiment.policy_folder_name = "ppo_policy_nr_extra_{config[nr_extra]}_"
+
+run_experiment.horizon = 50
+run_experiment.seed = 123
+
+#run_experiment.conv_model = {"grid_search": [True, False]}
+run_experiment.conv_model = False
+
+#run_experiment.obs_builder = {"grid_search": [@GlobalObsForRailEnv(), @GlobalObsForRailEnvDirectionDependent]}# [@TreeObsForRailEnv(), @GlobalObsForRailEnv() ]}
+run_experiment.obs_builder = @TreeObsForRailEnv()
+TreeObsForRailEnv.max_depth = 2
+LocalObsForRailEnv.view_radius = 5
+
+run_experiment.entropy_coeff = 0.01
+
diff --git a/RLLib_training/experiment_configs/observation_benchmark_loaded_env/config.gin b/RLLib_training/experiment_configs/observation_benchmark_loaded_env/config.gin
index 64ff1c981dc9d068dee3a089bc8cb77c834d9e63..1369bb44d9e6f6d761a2d7f4a37af11a735c1fab 100644
--- a/RLLib_training/experiment_configs/observation_benchmark_loaded_env/config.gin
+++ b/RLLib_training/experiment_configs/observation_benchmark_loaded_env/config.gin
@@ -4,8 +4,8 @@ run_experiment.save_every = 50
run_experiment.hidden_sizes = [32, 32]
run_experiment.map_width = 20
-run_experiment.map_height = 20
-run_experiment.n_agents = 5
+run_experiment.map_height = 10
+run_experiment.n_agents = 8
run_experiment.policy_folder_name = "ppo_policy_{config[obs_builder].__class__.__name__}"#_entropy_coeff_{config[entropy_coeff]}_{config[hidden_sizes][0]}_hidden_sizes_"
run_experiment.horizon = 50
diff --git a/RLLib_training/train_experiment.py b/RLLib_training/train_experiment.py
index d58e9bf0e02ee270e97672a42dd26384c68d7b4e..57fb0ceb642cabd562075c3aae8e7cd8e6240460 100644
--- a/RLLib_training/train_experiment.py
+++ b/RLLib_training/train_experiment.py
@@ -1,10 +1,10 @@
from baselines.RLLib_training.RailEnvRLLibWrapper import RailEnvRLLibWrapper
import gym
+import gin
from flatland.envs.generators import complex_rail_generator
-
# Import PPO trainer: we can replace these imports by any other trainer from RLLib.
from ray.rllib.agents.ppo.ppo import DEFAULT_CONFIG
from ray.rllib.agents.ppo.ppo import PPOTrainer as Trainer
@@ -25,8 +25,6 @@ import numpy as np
from ray.tune.logger import UnifiedLogger
import tempfile
-import gin
-
from ray import tune
from ray.rllib.utils.seed import seed as set_seed
@@ -59,14 +57,15 @@ def train(config, reporter):
# Example configuration to generate a random rail
env_config = {"width": config['map_width'],
"height": config['map_height'],
- "rail_generator": complex_rail_generator,
+ "rail_generator": config["rail_generator"],
+ "nr_extra": config["nr_extra"],
"number_of_agents": config['n_agents'],
"seed": config['seed'],
"obs_builder": config['obs_builder']}
# Observation space and action space definitions
if isinstance(config["obs_builder"], TreeObsForRailEnv):
- obs_space = gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(111,))
+ obs_space = gym.spaces.Box(low=-1, high=1, shape=(147,))
preprocessor = "tree_obs_prep"
elif isinstance(config["obs_builder"], GlobalObsForRailEnv):
@@ -166,7 +165,7 @@ def train(config, reporter):
@gin.configurable
def run_experiment(name, num_iterations, n_agents, hidden_sizes, save_every,
map_width, map_height, horizon, policy_folder_name, local_dir, obs_builder,
- entropy_coeff, seed, conv_model):
+ entropy_coeff, seed, conv_model, rail_generator, nr_extra):
tune.run(
train,
@@ -183,7 +182,9 @@ def run_experiment(name, num_iterations, n_agents, hidden_sizes, save_every,
"obs_builder": obs_builder,
"entropy_coeff": entropy_coeff,
"seed": seed,
- "conv_model": conv_model
+ "conv_model": conv_model,
+ "rail_generator": rail_generator,
+ "nr_extra": nr_extra
},
resources_per_trial={
"cpu": 2,
@@ -195,6 +196,6 @@ def run_experiment(name, num_iterations, n_agents, hidden_sizes, save_every,
if __name__ == '__main__':
gin.external_configurable(tune.grid_search)
- dir = '/mount/SDC/flatland/baselines/RLLib_training/experiment_configs/observation_benchmark_loaded_env' # To Modify
+ dir = '/home/guillaume/EPFL/Master_Thesis/flatland/baselines/RLLib_training/experiment_configs/env_complexity_benchmark' # To Modify
gin.parse_config_file(dir + '/config.gin')
run_experiment(local_dir=dir)