diff --git a/baselines/train.py b/baselines/train.py
deleted file mode 100644
index 5879052ffa9876a8ed11224e179d329bed2cd979..0000000000000000000000000000000000000000
--- a/baselines/train.py
+++ /dev/null
@@ -1,115 +0,0 @@
-from flatland.envs import rail_env
-from flatland.envs.rail_env import random_rail_generator
-from baselines.RailEnvRLLibWrapper import RailEnvRLLibWrapper
-from flatland.utils.rendertools import RenderTool
-import random
-import gym
-
-import matplotlib.pyplot as plt
-
-from flatland.envs.generators import complex_rail_generator
-
-import ray.rllib.agents.ppo.ppo as ppo
-import ray.rllib.agents.dqn.dqn as dqn
-from ray.rllib.agents.ppo.ppo import PPOAgent
-from ray.rllib.agents.dqn.dqn import DQNAgent
-from ray.rllib.agents.ppo.ppo_policy_graph import PPOPolicyGraph
-from ray.rllib.agents.dqn.dqn_policy_graph import DQNPolicyGraph
-
-from ray.tune.registry import register_env
-from ray.rllib.models import ModelCatalog
-from ray.tune.logger import pretty_print
-from ray.rllib.models.preprocessors import Preprocessor
-
-
-import ray
-import numpy as np
-
-from ray.rllib.env.multi_agent_env import MultiAgentEnv
-
-# RailEnv.__bases__ = (RailEnv.__bases__[0], MultiAgentEnv)
-
-
-class MyPreprocessorClass(Preprocessor):
- def _init_shape(self, obs_space, options):
- return (105,)
-
- def transform(self, observation):
- return observation # return the preprocessed observation
-
-ModelCatalog.register_custom_preprocessor("my_prep", MyPreprocessorClass)
-ray.init()
-
-def train(config):
- print('Init Env')
- random.seed(1)
- np.random.seed(1)
-
- # Example generate a rail given a manual specification,
- # a map of tuples (cell_type, rotation)
- transition_probability = [0.5, # empty cell - Case 0
- 1.0, # Case 1 - straight
- 1.0, # Case 2 - simple switch
- 0.3, # Case 3 - diamond drossing
- 0.5, # Case 4 - single slip
- 0.5, # Case 5 - double slip
- 0.2, # Case 6 - symmetrical
- 0.0] # Case 7 - dead end
-
-
-
- # Example generate a random rail
- env = RailEnvRLLibWrapper(width=15, height=15,
- rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=20, min_dist=12),
- number_of_agents=10)
-
- register_env("railenv", lambda _: env)
- # if config['render']:
- # env_renderer = RenderTool(env, gl="QT")
- # plt.figure(figsize=(5,5))
-
- 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["num_workers"] = 0
- 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"] = 20
- #agent_config["batch_mode"] = "complete_episodes"
-
- ppo_trainer = PPOAgent(env=f"railenv", 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({})
-
-
-
-
-
-