Add GATE wheel temporarily

src/primaite/game/agent/GATE_agents.py

@@ -1,5 +1,64 @@
 from primaite.game.agent.interface import AbstractGATEAgent
+from arcd_gate.client.gate_client import GATEClient
 
 
+class GATEMan(GATEClient):
+
+    @property
+    def rl_framework(self) -> str:
+        return "SB3"
+
+    @property
+    def rl_framework(self) -> str:
+        pass
+
+    @property
+    def rl_algorithm(self) -> str:
+        pass
+
+    @property
+    def seed(self) -> Optional[int]:
+        return None
+
+    @property
+    def n_learn_episodes(self) -> int:
+        return 0
+
+    @property
+    def n_learn_steps(self) -> int:
+        return 0
+
+    @property
+    def n_eval_episodes(self) -> int:
+        return 0
+
+    @property
+    def n_eval_steps(self) -> int:
+        return 0
+
+    @property
+    def action_space(self) -> spaces.Space:
+        pass
+
+    @property
+    def observation_space(self) -> spaces.Space:
+        pass
+
+    def step(self, action: ActType) -> Tuple[np.ndarray, float, bool, bool, Dict]:
+        pass
+
+    def reset(self, *, seed: Optional[int] = None, options: Optional[dict[str, Any]] = None) -> Tuple[np.ndarray, Dict]:
+        pass
+
+    def close(self):
+        pass
+
 class GATERLAgent(AbstractGATEAgent):
     ...
+    # The communication with GATE needs to be handled by the PrimaiteSession, rather than by individual agents,
+    # because when we are supporting MARL, the actions form multiple agents will have to be batched
+
+    # For example MultiAgentEnv in Ray allows sending a dict of observations of multiple agents, then it will reply
+    # with the actions for those agents.
+
+