Resolves #76 (2)

tests/units/agents/test_agent_base.py

@@ -0,0 +1,82 @@
+import unittest
+from abc import ABCMeta
+
+from urnai.actions.action_space_base import ActionSpaceBase
+from urnai.agents.agent_base import AgentBase
+from urnai.models.model_base import ModelBase
+from urnai.states.state_base import StateBase
+
+
+class FakeAgent(AgentBase):
+    AgentBase.__abstractmethods__ = set()
+
+    def __init__(self, action_space,
+                 state_space : StateBase,
+                 model : ModelBase,
+                 reward):
+        super().__init__(action_space, state_space, model, reward)
+
+class FakeState(StateBase):
+    StateBase.__abstractmethods__ = set()
+
+class FakeModel(ModelBase):
+    ModelBase.__abstractmethods__ = set()
+
+    def ep_reset(self, ep):
+        return None
+
+class FakeReward:
+    def get_reward(self, obs, reward, done):
+        return None
+    def reset(self):
+        return None
+
+class FakeActionSpace(ActionSpaceBase):
+    ActionSpaceBase.__abstractmethods__ = set()
+
+class TestAgentBase(unittest.TestCase):
+
+    def test_abstract_methods(self):
+        # GIVEN
+        fake_agent = FakeAgent(None, None, None, None)
+
+        # WHEN
+        step_return = fake_agent.step()
+        choose_action_return = fake_agent.choose_action(FakeActionSpace())
+
+        # THEN
+        assert isinstance(AgentBase, ABCMeta)
+        assert step_return is None
+        assert choose_action_return is None
+
+    def test_reset(self):
+        # GIVEN
+        fake_action_space = FakeActionSpace()
+        fake_state_space = FakeState()
+        fake_model = FakeModel()
+        fake_reward = FakeReward()
+        fake_agent = FakeAgent(
+            fake_action_space,
+            fake_state_space,
+            fake_model,
+            fake_reward
+        )
+
+        # WHEN
+        reset_return = fake_agent.reset()
+
+        # THEN
+        assert reset_return is None
+        assert fake_agent.previous_action is None
+        assert fake_agent.previous_state is None
+
+    def test_learn(self):
+        # GIVEN
+        fake_model = FakeModel()
+        fake_agent = FakeAgent(None, None, fake_model, None)
+
+        # WHEN
+        learn_return = fake_agent.learn("obs", "reward", "done")
+
+        # THEN
+        assert learn_return is None

urnai/agents/agent_base.py

@@ -0,0 +1,59 @@
+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.states.state_base import StateBase
+
+
+class AgentBase(ABC):
+
+    def __init__(self, action_space : ActionSpaceBase,
+                 state_space : StateBase,
+                 model : ModelBase,
+                 reward):
+
+        self.action_space = action_space
+        self.state_space = state_space
+        self.model = model
+        self.reward = reward
+
+        self.previous_action = None
+        self.previous_state = None
+
+        self.attr_block_list = ['model', 'action_space',
+                                'state_space', 'reward']
+
+    @abstractmethod
+    def step(self) -> None:
+        ...
+
+    @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:
+        """
+        Resets some Agent class variables, such as previous_action
+        and previous_state. Also, calls the respective reset methods
+        for the action_wrapper and model.
+        """
+        self.previous_action = None
+        self.previous_state = None
+        self.action_space.reset()
+        self.model.ep_reset(episode)
+        self.reward.reset()
+        self.state_space.reset()
+
+    def learn(self, obs, reward, done) -> None:
+        """
+        If it is not the very first step in an episode, this method will
+        call the model's learn method.
+        """
+        if self.previous_state is not None:
+            next_state = self.update_state(obs)
+            self.model.learn(self.previous_state, self.previous_action,
+                              reward, next_state, done)