Finalise actions interface

src/primaite/game/session.py

@@ -4,3 +4,49 @@
 # 3. create actors and configure their actions/observations/rewards/ anything else
 # 4. Create connection with ARCD GATE
 # 5. idk
+
+from primaite.simulator.sim_container import Simulation
+from primaite.game.agent.interface import AbstractAgent
+
+from typing import List
+
+class PrimaiteSession:
+    def __init__(self):
+        self.simulation: Simulation = Simulation()
+        self.agents:List[AbstractAgent] = []
+        self.step_counter:int = 0
+        self.episode_counter:int = 0
+
+
+    def step(self):
+        # currently designed with assumption that all agents act once per step in order
+
+
+        for agent in self.agents:
+            # 3. primaite session asks simulation to provide initial state
+            # 4. primate session gives state to all agents
+            # 5. primaite session asks agents to produce an action based on most recent state
+            sim_state = self.simulation.describe_state()
+
+            # 6. each agent takes most recent state and converts it to CAOS observation
+            agent_obs = agent.get_obs_from_state(sim_state)
+
+            # 7. meanwhile each agent also takes state and calculates reward
+            agent_reward = agent.get_reward_from_state(sim_state)
+
+            # 8. each agent takes observation and applies decision rule to observation to create CAOS
+            #    action(such as random, rulebased, or send to GATE) (therefore, converting CAOS action
+            #    to discrete(40) is only necessary for purposes of RL learning, therefore that bit of
+            #    code should live inside of the GATE agent subclass)
+            # gets action in CAOS format
+            agent_action = agent.get_action(agent_obs, agent_reward)
+            # 9. CAOS action is converted into request (extra information might be needed to enrich
+            # the request, this is what the execution definition is there for)
+            agent_request = agent.format_request(agent_action)
+
+            # 10. primaite session receives the action from the agents and asks the simulation to apply each
+            self.simulation.apply_action(agent_request)
+
+        self.simulation.apply_timestep(self.step_counter)
+        self.step_counter += 1
+