Delete train.py

RLLib_training/train.py

-import random
-
-import gym
-import numpy as np
-import ray
-import ray.rllib.agents.ppo.ppo as ppo
-from RailEnvRLLibWrapper import RailEnvRLLibWrapper
-from flatland.envs.generators import complex_rail_generator
-from ray.rllib.agents.ppo.ppo import PPOTrainer
-from ray.rllib.agents.ppo.ppo_policy_graph import PPOPolicyGraph
-from ray.rllib.models import ModelCatalog
-from ray.tune.logger import pretty_print
-
-from RLLib_training.custom_preprocessors import CustomPreprocessor
-
-ModelCatalog.register_custom_preprocessor("my_prep", CustomPreprocessor)
-ray.init()
-
-
-def train(config):
-    print('Init Env')
-    random.seed(1)
-    np.random.seed(1)
-
-    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_config = {"width": 20,
-                  "height": 20,
-                  "rail_generator": complex_rail_generator(nr_start_goal=5, min_dist=5, max_dist=99999, seed=0),
-                  "number_of_agents": 5}
-
-    obs_space = gym.spaces.Box(low=-float('inf'), high=float('inf'), shape=(105,))
-    act_space = gym.spaces.Discrete(4)
-
-    # Dict with the different policies to train
-    policy_graphs = {
-        "ppo_policy": (PPOPolicyGraph, obs_space, act_space, {})
-    }
-
-    def policy_mapping_fn(agent_id):
-        return f"ppo_policy"
-
-    agent_config = ppo.DEFAULT_CONFIG.copy()
-    agent_config['model'] = {"fcnet_hiddens": [32, 32], "custom_preprocessor": "my_prep"}
-    agent_config['multiagent'] = {"policy_graphs": policy_graphs,
-                                  "policy_mapping_fn": policy_mapping_fn,
-                                  "policies_to_train": list(policy_graphs.keys())}
-    agent_config["horizon"] = 50
-    agent_config["num_workers"] = 0
-    # agent_config["sample_batch_size"]: 1000
-    # agent_config["num_cpus_per_worker"] = 40
-    # agent_config["num_gpus"] = 2.0
-    # agent_config["num_gpus_per_worker"] = 2.0
-    # agent_config["num_cpus_for_driver"] = 5
-    # agent_config["num_envs_per_worker"] = 15
-    agent_config["env_config"] = env_config
-    # agent_config["batch_mode"] = "complete_episodes"
-
-    ppo_trainer = PPOTrainer(env=RailEnvRLLibWrapper, config=agent_config)
-
-    for i in range(100000 + 2):
-        print("== Iteration", i, "==")
-
-        print("-- PPO --")
-        print(pretty_print(ppo_trainer.train()))
-
-        # if i % config['save_every'] == 0:
-        #     checkpoint = ppo_trainer.save()
-        #     print("checkpoint saved at", checkpoint)
-
-
-train({})