Merge pull request #35 from JdeRobot/fixture/entrypoint

Fixture/entrypoint

rl_studio/README.md

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+# Run RL Studio
+
+## Usage
+
+You can run a simple RLStudio trainer typing:
+
+```bash
+python main_rlstudio.py -n qlearn -a f1 -e simple -f config.yaml 
+```
+
+Open the `config.yaml` file and set the params you need.
+
+## Project diagram
+
+![](./docs/rlstudio-diagram.png)

rl_studio/agents/__init__.py

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+from rl_studio.agents.agents_type import AgentsType
+from rl_studio.agents.exceptions import NoValidTrainingType
+
+
+class TrainerFactory:
+    def __new__(cls, config):
+
+        agent = config.agent["name"]
+
+        if agent == AgentsType.F1.value:
+            from rl_studio.agents.f1.train_qlearn import F1Trainer
+            return F1Trainer(config)
+
+        elif agent == AgentsType.TURTLEBOT.value:
+            from rl_studio.agents.turtlebot.turtlebot_trainer import TurtlebotTrainer
+
+            return TurtlebotTrainer(config)
+
+        else:
+            raise NoValidTrainingType(agent)

rl_studio/agents/agents_type.py

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+from enum import Enum
+
+
+class AgentsType(Enum):
+    F1 = "f1"
+    TURTLEBOT = "turtlebot"

rl_studio/agents/exceptions.py

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+class CreationError(Exception):
+    ...
+
+
+class NoValidTrainingType(CreationError):
+    def __init__(self, training_type):
+        self.traning_type = training_type
+        self.message = f"[ERROR] No valid training type ({training_type}) in your config.yml file or is missing."
+        super().__init__(self.message)

rl_studio/agents/f1/__init__.py

@@ -1 +0,0 @@
-from rl_studio.envs.gazebo_env import *