feat: Added initial trainer to solve collectables

sc2_collectables_trainer.py

@@ -0,0 +1,48 @@
+import pathlib
+import sys
+
+sys.path.insert(0, str(pathlib.Path(__file__).parent.parent.parent))
+
+from absl import app
+from pysc2.env import sc2_env
+
+from urnai.models.dqn_pytorch import DQNPytorch
+from urnai.sc2.actions.collectables import CollectablesActionSpace
+from urnai.sc2.agents.sc2_agent import SC2Agent
+from urnai.sc2.environments.sc2environment import SC2Env
+from urnai.sc2.rewards.collectables import CollectablesReward
+from urnai.sc2.states.collectables import CollectablesState
+from urnai.trainers.trainer import Trainer
+
+
+def declare_trainer():
+    players = [sc2_env.Agent(sc2_env.Race.terran)]
+    env = SC2Env(map_name='CollectMineralShards', visualize=False, 
+                 step_mul=16, players=players)
+
+
+    action_space = CollectablesActionSpace()
+    state_builder = CollectablesState()
+    reward_builder = CollectablesReward()
+
+    model = DQNPytorch(action_space, state_builder)
+
+    agent = SC2Agent(action_space, state_builder, model, reward_builder)
+
+    trainer = Trainer(env, agent,
+                      max_training_episodes=200, max_steps_training=100000,
+                      max_playing_episodes=200, max_steps_playing=100000)
+    return trainer
+
+def main(unused_argv):
+    try:
+        trainer = declare_trainer()
+        trainer.train()
+        # trainer.play()
+
+    except KeyboardInterrupt:
+        pass
+
+
+if __name__ == '__main__':
+    app.run(main)

urnai/agents/agent_base.py

@@ -1,8 +1,8 @@
 from abc import ABC, abstractmethod
 
-from urnai.actions.action_base import ActionBase
 from urnai.actions.action_space_base import ActionSpaceBase
 from urnai.models.model_base import ModelBase
+from urnai.rewards.reward_base import RewardBase
 from urnai.states.state_base import StateBase
 
 
@@ -11,7 +11,7 @@ class AgentBase(ABC):
     def __init__(self, action_space : ActionSpaceBase,
                  state_space : StateBase,
                  model : ModelBase,
-                 reward):
+                 reward : RewardBase):
 
         self.action_space = action_space
         self.state_space = state_space
@@ -28,12 +28,12 @@ def __init__(self, action_space : ActionSpaceBase,
     def step(self) -> None:
         ...
 
-    @abstractmethod
-    def choose_action(self, action_space : ActionSpaceBase) -> ActionBase:
-        """
-        Method that contains the agent's strategy for choosing actions
-        """
-        ...
+    # @abstractmethod
+    # def choose_action(self, action_space : ActionSpaceBase) -> ActionBase:
+    #     """
+    #     Method that contains the agent's strategy for choosing actions
+    #     """
+    #     ...
 
     def reset(self, episode=0) -> None:
         """