feat(*): Add default bot code

bot/action/continuous_act.py

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+import numpy as np
+from rlgym_compat import GameState
+
+
+class ContinuousAction:
+    """
+        Simple continuous action space. Operates in the range -1 to 1, even for the binary actions which are converted back to binary later.
+        This is for improved compatibility, stable baselines doesn't support tuple spaces right now.
+    """
+
+    def __init__(self):
+        pass
+
+    def get_action_space(self):
+        raise NotImplementedError("We don't implement get_action_space to remove the gym dependency")
+
+    def parse_actions(self, actions: np.ndarray, state: GameState) -> np.ndarray:
+        actions = actions.reshape((-1, 8))
+
+        actions[..., :5] = actions[..., :5].clip(-1, 1)
+        # The final 3 actions handle are jump, boost and handbrake. They are inherently discrete so we convert them to either 0 or 1.
+        actions[..., 5:] = actions[..., 5:] > 0
+
+        return actions

bot/action/default_act.py

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+import numpy as np
+from rlgym_compat import GameState
+from .continuous_act import ContinuousAction
+from typing import Union, List
+
+
+class DefaultAction(ContinuousAction):
+    """
+        Continuous Action space, that also accepts a few other input formats for QoL reasons and to remain
+        compatible with older versions.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def get_action_space(self):
+        return super().get_action_space()
+
+    def parse_actions(self, actions: Union[np.ndarray, List[np.ndarray], List[float]], state: GameState) -> np.ndarray:
+
+        # allow other data types, this part should not be necessary but is nice to have in the default action parser.
+        if type(actions) != np.ndarray:
+            actions = np.asarray(actions)
+
+        if len(actions.shape) == 1:
+            actions = actions.reshape((-1, 8))
+        elif len(actions.shape) > 2:
+            raise ValueError('{} is not a valid action shape'.format(actions.shape))
+
+        return super().parse_actions(actions, state)

bot/action/discrete_act.py

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+import numpy as np
+from rlgym_compat import GameState
+
+
+class DiscreteAction:
+    """
+    Simple discrete action space. All the analog actions have 3 bins by default: -1, 0 and 1.
+    """
+
+    def __init__(self, n_bins=3):
+        assert n_bins % 2 == 1, "n_bins must be an odd number"
+        self._n_bins = n_bins
+
+    def get_action_space(self):
+        raise NotImplementedError("We don't implement get_action_space to remove the gym dependency")
+
+    def parse_actions(self, actions: np.ndarray, state: GameState) -> np.ndarray:
+        actions = actions.reshape((-1, 8)).astype(dtype=np.float32)
+
+        # map all binned actions from {0, 1, 2 .. n_bins - 1} to {-1 .. 1}.
+        actions[..., :5] = actions[..., :5] / (self._n_bins // 2) - 1
+
+        return actions
+