Create a dataset using an agent (#306)

Generate a dataset using an agent (allowing to select between this and a random dataset)

rl_coach/exploration_policies/exploration_policy.py

@@ -18,7 +18,7 @@ from typing import List
 
 from rl_coach.base_parameters import Parameters
 from rl_coach.core_types import RunPhase, ActionType
-from rl_coach.spaces import ActionSpace
+from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace, GoalsSpace
 
 
 class ExplorationParameters(Parameters):
@@ -54,14 +54,10 @@ class ExplorationPolicy(object):
         Given a list of values corresponding to each action, 
         choose one actions according to the exploration policy
         :param action_values: A list of action values
-        :return: The chosen action
+        :return: The chosen action,
+                 The probability of the action (if available, otherwise 1 for absolute certainty in the action)
         """
-        if self.__class__ == ExplorationPolicy:
-            raise ValueError("The ExplorationPolicy class is an abstract class and should not be used directly. "
-                             "Please set the exploration parameters to point to an inheriting class like EGreedy or "
-                             "AdditiveNoise")
-        else:
-            raise ValueError("The get_action function should be overridden in the inheriting exploration class")
+        raise NotImplementedError()
 
     def change_phase(self, phase):
         """
@@ -82,3 +78,42 @@ class ExplorationPolicy(object):
 
     def get_control_param(self):
         return 0
+
+
+class DiscreteActionExplorationPolicy(ExplorationPolicy):
+    """
+    A discrete action exploration policy.
+    """
+    def __init__(self, action_space: ActionSpace):
+        """
+        :param action_space: the action space used by the environment
+        """
+        assert isinstance(action_space, DiscreteActionSpace)
+        super().__init__(action_space)
+
+    def get_action(self, action_values: List[ActionType]) -> (ActionType, List):
+        """
+        Given a list of values corresponding to each action,
+        choose one actions according to the exploration policy
+        :param action_values: A list of action values
+        :return: The chosen action,
+                 The probabilities of actions to select from (if not available a one-hot vector)
+        """
+        if self.__class__ == ExplorationPolicy:
+            raise ValueError("The ExplorationPolicy class is an abstract class and should not be used directly. "
+                             "Please set the exploration parameters to point to an inheriting class like EGreedy or "
+                             "AdditiveNoise")
+        else:
+            raise ValueError("The get_action function should be overridden in the inheriting exploration class")
+
+
+class ContinuousActionExplorationPolicy(ExplorationPolicy):
+    """
+    A continuous action exploration policy.
+    """
+    def __init__(self, action_space: ActionSpace):
+        """
+        :param action_space: the action space used by the environment
+        """
+        assert isinstance(action_space, BoxActionSpace) or isinstance(action_space, GoalsSpace)
+        super().__init__(action_space)