Merge branch 'master' of gitlab.aicrowd.com:flatland/baselines

RLLib_training/custom_preprocessors.py

@@ -55,12 +55,14 @@ class CustomPreprocessor(Preprocessor):
         # return ((sum([space.shape[0] for space in obs_space[:2]]) + obs_space[2].shape[0] * obs_space[2].shape[1]),)
 
     def transform(self, observation):
-        print('OBSSSSSSSSSSSSSSSSSs', observation, observation.shape)
-        data = norm_obs_clip(observation[0])
-        distance = norm_obs_clip(observation[1])
-        agent_data = np.clip(observation[2], -1, 1)
-
-        return np.concatenate((np.concatenate((data, distance)), agent_data))
+        data = norm_obs_clip(observation[0][0])
+        distance = norm_obs_clip(observation[0][1])
+        agent_data = np.clip(observation[0][2], -1, 1)
+        data2 = norm_obs_clip(observation[1][0])
+        distance2 = norm_obs_clip(observation[1][1])
+        agent_data2 = np.clip(observation[1][2], -1, 1)
+
+        return np.concatenate((np.concatenate((np.concatenate((data, distance)), agent_data)), np.concatenate((np.concatenate((data2, distance2)), agent_data2))))
         return norm_obs_clip(observation)
         return np.concatenate([norm_obs_clip(observation[0]), norm_obs_clip(observation[1])])
         # if len(observation) == 111:

RLLib_training/experiment_configs/env_size_benchmark_3_agents/config.gin

@@ -3,12 +3,12 @@ run_experiment.num_iterations = 2002
 run_experiment.save_every = 100
 run_experiment.hidden_sizes = [32, 32]
 
-run_experiment.map_width = 20
-run_experiment.map_height = 20
+run_experiment.map_width = 40
+run_experiment.map_height = 40
 run_experiment.n_agents = {"grid_search": [3, 4, 5, 6, 7, 8]}
 run_experiment.rail_generator = "complex_rail_generator"
 run_experiment.nr_extra = 5
-run_experiment.policy_folder_name = "ppo_policy_two_obs_with_predictions_n_agents_{config[n_agents]}_"
+run_experiment.policy_folder_name = "ppo_policy_two_obs_with_predictions_n_agents_{config[n_agents]}__map_size_{config[map_width]}"
 
 #run_experiment.horizon =
 run_experiment.seed = 123
@@ -18,12 +18,13 @@ run_experiment.conv_model = False
 
 #run_experiment.obs_builder = {"grid_search": [@GlobalObsForRailEnv(), @GlobalObsForRailEnvDirectionDependent]}# [@TreeObsForRailEnv(), @GlobalObsForRailEnv() ]}
 run_experiment.obs_builder = @TreeObsForRailEnv()
-TreeObsForRailEnv.predictor = @ShortestPathPredictorForRailEnv
+TreeObsForRailEnv.predictor = @ShortestPathPredictorForRailEnv()
 TreeObsForRailEnv.max_depth = 2
 LocalObsForRailEnv.view_radius = 5
 
 run_experiment.entropy_coeff = 0.001
 run_experiment.kl_coeff = 0.2 #{"grid_search": [0, 0.2]}
 run_experiment.lambda_gae = 0.9 # {"grid_search": [0.9, 1.0]}
-#run_experiment.predictor = "ShortestPathPredictorForRailEnv"
+#run_experiment.predictor = "ShortestPathPredictorForRailEnv()"
 run_experiment.step_memory = 2
+run_experiment.min_dist = 10

RLLib_training/train_experiment.py

@@ -80,12 +80,11 @@ def train(config, reporter):
                   "seed": config['seed'],
                   "obs_builder": config['obs_builder'],
                   "min_dist": config['min_dist'],
-                  "predictor": config["predictor"],
                   "step_memory": config["step_memory"]}
 
     # Observation space and action space definitions
     if isinstance(config["obs_builder"], TreeObsForRailEnv):
-        obs_space = gym.spaces.Tuple((gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(168,)), ))
+        obs_space = gym.spaces.Tuple((gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(168,)),) * 2)
         preprocessor = "tree_obs_prep"
 
     elif isinstance(config["obs_builder"], GlobalObsForRailEnv):
@@ -190,7 +189,7 @@ 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,
+                   map_width, map_height, policy_folder_name, local_dir, obs_builder,
                    entropy_coeff, seed, conv_model, rail_generator, nr_extra, kl_coeff, lambda_gae,
                    step_memory, min_dist):
     tune.run(
@@ -203,7 +202,6 @@ def run_experiment(name, num_iterations, n_agents, hidden_sizes, save_every,
                 "map_width": map_width,
                 "map_height": map_height,
                 "local_dir": local_dir,
-                "horizon": horizon,  # Max number of time steps
                 'policy_folder_name': policy_folder_name,
                 "obs_builder": obs_builder,
                 "entropy_coeff": entropy_coeff,
@@ -230,7 +228,7 @@ if __name__ == '__main__':
     gin.external_configurable(tune.grid_search)
     # with path('RLLib_training.experiment_configs.n_agents_experiment', 'config.gin') as f:
     #     gin.parse_config_file(f)
-    gin.parse_config_file('/home/guillaume/flatland/baselines/RLLib_training/experiment_configs/score_metric_test/config.gin')
-    dir = '/home/guillaume/flatland/baselines/RLLib_training/experiment_configs/score_metric_test'
+    gin.parse_config_file('/mount/SDC/flatland/baselines/RLLib_training/experiment_configs/env_size_benchmark_3_agents/config.gin')
+    dir = '/mount/SDC/flatland/baselines/RLLib_training/experiment_configs/env_size_benchmark_3_agents'
     # dir = os.path.join(__file_dirname__, 'experiment_configs', 'experiment_agent_memory')
     run_experiment(local_dir=dir)