def simulate()

in source/MXNetEnv/heuristics_utils.py [0:0]

• 62 lines of code
• 8 McCabe index (conditional complexity)

def simulate(env, net, heuristics, number_of_snakes):
    '''
    Helper functions to simulate the snakes moving with BattlesnakeGym.
    Pseudo code is:
    Until only 1 snake is alive:
        1. Get actions from the neural network
        2. Run heuristisc
        3. Feed into the battlesnake gym
    '''
    Memory = namedtuple("Memory", "state turn_count health")
    
    state, _, _, infos = env.reset()
    
    rgb_arrays = [env.render(mode="rgb_array")]
    infos_array = [infos]
    actions_array = [[4, 4, 4, 4]]
    json_array = [env.get_json()]
        
    heuristics_log_array = [{k: "" for k in range(number_of_snakes)}]

    memory = Memory(state=np.zeros(state.shape), turn_count=infos["current_turn"], health=infos["snake_health"])
    while True:
        infos["current_turn"] += 1

        heuristics_log = {}
        actions = []
        for i in range(number_of_snakes):
            action = get_action(net, state, snake_id=i,
                                turn_count=infos["current_turn"]+1,
                                health=infos["snake_health"],
                                memory=memory)

            snake_list = make_snake_lists(env)
            map_size = env.map_size
            json = convert_state_into_json(map_size, state, snake_list, snake_id=i, 
                                           turn_count=infos["current_turn"]+1, 
                                           health=infos["snake_health"])
            # Add heuristics
            if is_snake_alive(env, i):
                action, heuristics_log_string = heuristics.run(
                                                state, snake_id=i,
                                                turn_count=infos["current_turn"]+1,
                                                health=infos["snake_health"],
                                                json=json,
                                                action=action)           
            else:
                action = np.argmax(action[0])
                heuristics_log_string = "Dead"
            heuristics_log[i] = heuristics_log_string
            
            actions.append(action)
        memory = Memory(state=state, turn_count=infos["current_turn"], 
                        health=infos["snake_health"])
        
        next_state, reward, dones, infos = env.step(np.array(actions))
        
        rgb_array = env.render(mode="rgb_array")
        rgb_arrays.append(rgb_array.copy())
        infos_array.append(infos)
        actions_array.append(actions)
        heuristics_log_array.append(heuristics_log)
        json_array.append(env.get_json())
        
        # Check if only 1 snake remains
        number_of_snakes_alive = sum(list(dones.values()))
        if number_of_snakes - number_of_snakes_alive <= 1:
            done = True
        else:
            done = False

        state = next_state
        if done:
            print("Completed")
            break  

    return infos_array, rgb_arrays, actions_array, heuristics_log_array, json_array