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

RLLib_training/README.md

 This repository allows to run Rail Environment multi agent training with the RLLib Library.
 
 ## Installation:
+
+To run scripts of this repository, the deep learning library tensorflow should be installed, along with the following packages:
 ```sh
-pip install ray
-pip install gin-config
+pip install gym ray==0.7.0 gin-config opencv-python lz4 psutil
 ```
 
 To start a training with different parameters, you can create a folder containing a config.gin file (see example in `experiment_configs/config_example/config.gin`.
@@ -57,7 +58,7 @@ More documentation on how to use gin-config can be found on the github repositor
 ## Run an example:
 To start a training on a 20X20 map, with different numbers of agents initialized at each episode, on can run the train_experiment.py script:
 ```
-python baselines/RLLib_training/train_experiment.py
+python RLLib_training/train_experiment.py
 ```
 This will load the gin config file in the folder `experiment_configs/config_examples`.
 

sequential_agent/run_test.py

@@ -8,11 +8,11 @@ import numpy as np
 
 np.random.seed(2)
 """
-file_name = "./railway/complex_scene.pkl"
+file_name = "../torch_training/railway/complex_scene.pkl"
 env = RailEnv(width=10,
               height=20,
               rail_generator=rail_from_file(file_name),
-              obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()))
+              obs_builder_object=TreeObsForRailEnv(max_depth=1, predictor=ShortestPathPredictorForRailEnv()))
 x_dim = env.width
 y_dim = env.height
 
@@ -38,8 +38,8 @@ observation_helper = TreeObsForRailEnv(max_depth=tree_depth, predictor=ShortestP
 env_renderer = RenderTool(env, gl="PILSVG", )
 handle = env.get_agent_handles()
 n_trials = 1
-max_steps = 3 * (env.height + env.width)
-record_images = True
+max_steps = 100 * (env.height + env.width)
+record_images = False
 agent = OrderedAgent()
 action_dict = dict()
 
@@ -63,6 +63,7 @@ for trials in range(1, n_trials + 1):
         for a in range(env.get_num_agents()):
             if done[a]:
                 acting_agent += 1
+                print(acting_agent)
             if a == acting_agent:
                 action = agent.act(obs[a], eps=0)
             else:

sequential_agent/simple_order_agent.py

@@ -18,7 +18,7 @@ class OrderedAgent:
         min_dist = min_lt(distance, 0)
         min_direction = np.where(distance == min_dist)
         if len(min_direction[0]) > 1:
-            return min_direction[0][0] + 1
+            return min_direction[0][-1] + 1
         return min_direction[0] + 1
 
     def step(self, memories):

torch_training/multi_agent_inference.py

@@ -16,7 +16,7 @@ from utils.observation_utils import normalize_observation
 
 random.seed(3)
 np.random.seed(2)
-
+"""
 file_name = "./railway/complex_scene.pkl"
 env = RailEnv(width=10,
               height=20,
@@ -27,9 +27,9 @@ y_dim = env.height
 
 """
 
-x_dim = 10  # np.random.randint(8, 20)
-y_dim = 10  # np.random.randint(8, 20)
-n_agents = 5  # np.random.randint(3, 8)
+x_dim = 18  # np.random.randint(8, 20)
+y_dim = 14  # np.random.randint(8, 20)
+n_agents = 7  # np.random.randint(3, 8)
 n_goals = n_agents + np.random.randint(0, 3)
 min_dist = int(0.75 * min(x_dim, y_dim))
 
@@ -41,7 +41,7 @@ env = RailEnv(width=x_dim,
               obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()),
               number_of_agents=n_agents)
 env.reset(True, True)
-"""
+
 tree_depth = 3
 observation_helper = TreeObsForRailEnv(max_depth=tree_depth, predictor=ShortestPathPredictorForRailEnv())
 env_renderer = RenderTool(env, gl="PILSVG", )
@@ -53,7 +53,7 @@ for i in range(tree_depth + 1):
 state_size = num_features_per_node * nr_nodes
 action_size = 5
 
-n_trials = 1
+n_trials = 10
 observation_radius = 10
 max_steps = int(3 * (env.height + env.width))
 eps = 1.
@@ -70,7 +70,7 @@ action_prob = [0] * action_size
 agent_obs = [None] * env.get_num_agents()
 agent_next_obs = [None] * env.get_num_agents()
 agent = Agent(state_size, action_size, "FC", 0)
-with path(torch_training.Nets, "avoid_checkpoint52800.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint46200.pth") as file_in:
     agent.qnetwork_local.load_state_dict(torch.load(file_in))
 
 record_images = False
@@ -98,12 +98,11 @@ for trials in range(1, n_trials + 1):
         for a in range(env.get_num_agents()):
             action = agent.act(agent_obs[a], eps=0)
             action_dict.update({a: action})
-
         # Environment step
 
         next_obs, all_rewards, done, _ = env.step(action_dict)
         for a in range(env.get_num_agents()):
-            agent_obs[a] = agent_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
+            agent_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
 
         if done['__all__']:
             break