support config of util function via cli

deepcomp/env/entities/user.py

@@ -6,15 +6,15 @@
 from matplotlib import cm
 
 from deepcomp.env.util.utility import log_utility, step_utility
-from deepcomp.util.constants import MIN_UTILITY, MAX_UTILITY
+from deepcomp.util.constants import MIN_UTILITY, MAX_UTILITY, SUPPORTED_UTILITIES
 
 
 class User:
     """
     A user/UE moving around in the world and requesting mobile services
     Connection to BS are checked before connecting and after every move to check if connection is lost or still stable
     """
-    def __init__(self, id, map, pos_x, pos_y, movement, dr_req=1):
+    def __init__(self, id, map, pos_x, pos_y, movement, util_func='log', dr_req=1):
         """
         Create new UE object
         :param id: Unique ID of UE (string)
@@ -27,6 +27,9 @@ def __init__(self, id, map, pos_x, pos_y, movement, dr_req=1):
         self.id = id
         self.map = map
         self.movement = movement
+        assert util_func in SUPPORTED_UTILITIES, \
+            f"Utility function {util_func} not supported. Supported: {SUPPORTED_UTILITIES}"
+        self.util_func = util_func
         self.dr_req = dr_req
         # dict of connected BS: BS (only connected BS are keys!) --> data rate of connection
         self.bs_dr = {}
@@ -77,8 +80,14 @@ def utility(self):
 
     def dr_to_utility(self, dr):
         """Utility function to map given data rate to utility for the UE"""
-        # return step_utility(dr, self.dr_req)
-        return log_utility(dr)
+        assert self.util_func in SUPPORTED_UTILITIES, \
+            f"Utility function {self.util_func} not supported. Supported: {SUPPORTED_UTILITIES}"
+        if self.util_func == 'log':
+            return log_utility(dr)
+        if self.util_func == 'step':
+            return step_utility(dr, self.dr_req)
+        # unknown utility not implemented
+        raise NotImplementedError(f"Utility function {self.util_func} not implemented!")
 
     def seed(self, seed=None):
         self.rng.seed(seed)

deepcomp/util/cli.py

@@ -3,7 +3,7 @@
 import structlog
 
 from deepcomp.util.constants import SUPPORTED_ALGS, SUPPORTED_ENVS, SUPPORTED_AGENTS, SUPPORTED_RENDER, \
-    SUPPORTED_SHARING, SUPPORTED_REWARDS, CENTRAL_ALGS, MULTI_ALGS, SUPPORTED_UE_ARRIVAL
+    SUPPORTED_SHARING, SUPPORTED_REWARDS, CENTRAL_ALGS, MULTI_ALGS, SUPPORTED_UE_ARRIVAL, SUPPORTED_UTILITIES
 
 
 log = structlog.get_logger()
@@ -53,6 +53,7 @@ def setup_cli():
     parser.add_argument('--ue-arrival', type=str, choices=SUPPORTED_UE_ARRIVAL, help="UE arrival sequence")
     parser.add_argument('--sharing', type=str, choices=SUPPORTED_SHARING.union({'mixed'}), default='mixed',
                         help="Sharing model used by BS to split resources and/or rate among connected UEs.")
+    parser.add_argument('--util', type=str, choices=SUPPORTED_UTILITIES, default='log', help="UEs' utility function")
     # evaluation
     parser.add_argument('--rand-train', action='store_true', help="Randomize training episodes.")
     parser.add_argument('--cont-train', action='store_true', help="Continuous training without resetting.")

deepcomp/util/constants.py

@@ -22,6 +22,7 @@
 SUPPORTED_RENDER = {'html', 'gif', 'both', None}
 SUPPORTED_SHARING = {'max-cap', 'resource-fair', 'rate-fair', 'proportional-fair'}
 SUPPORTED_REWARDS = {'min', 'sum'}
+SUPPORTED_UTILITIES = {'log', 'step'}
 
 # small epsilon used in denominator to avoid division by zero
 EPSILON = 1e-16

deepcomp/util/env_setup.py

@@ -5,7 +5,8 @@
 from ray.rllib.agents.ppo import DEFAULT_CONFIG
 from ray.rllib.env.multi_agent_env import MultiAgentEnv
 
-from deepcomp.util.constants import SUPPORTED_ENVS, SUPPORTED_AGENTS, SUPPORTED_SHARING, SUPPORTED_UE_ARRIVAL
+from deepcomp.util.constants import SUPPORTED_ENVS, SUPPORTED_AGENTS, SUPPORTED_SHARING, SUPPORTED_UE_ARRIVAL, \
+    SUPPORTED_UTILITIES
 from deepcomp.env.single_ue.variants import RelNormEnv
 from deepcomp.env.multi_ue.central import CentralRelNormEnv
 from deepcomp.env.multi_ue.multi_agent import MultiAgentMobileEnv
@@ -141,18 +142,21 @@ def create_dyn_large_map(sharing_model, num_bs, dist_to_border=10):
     return map, bs_list
 
 
-def create_ues(map, num_static_ues, num_slow_ues, num_fast_ues):
+def create_ues(map, num_static_ues, num_slow_ues, num_fast_ues, util_func):
     """Create custom number of slow/fast UEs on the given map. Return UE list"""
     ue_list = []
     id = 1
     for i in range(num_static_ues):
-        ue_list.append(User(str(id), map, pos_x='random', pos_y='random', movement=RandomWaypoint(map, velocity=0)))
+        ue_list.append(User(str(id), map, pos_x='random', pos_y='random', movement=RandomWaypoint(map, velocity=0),
+                            util_func=util_func))
         id += 1
     for i in range(num_slow_ues):
-        ue_list.append(User(str(id), map, pos_x='random', pos_y='random', movement=RandomWaypoint(map, velocity='slow')))
+        ue_list.append(User(str(id), map, pos_x='random', pos_y='random', movement=RandomWaypoint(map, velocity='slow'),
+                            util_func=util_func))
         id += 1
     for i in range(num_fast_ues):
-        ue_list.append(User(str(id), map, pos_x='random', pos_y='random', movement=RandomWaypoint(map, velocity='fast')))
+        ue_list.append(User(str(id), map, pos_x='random', pos_y='random', movement=RandomWaypoint(map, velocity='fast'),
+                            util_func=util_func))
         id += 1
     return ue_list
 
@@ -169,18 +173,14 @@ def create_custom_env(sharing_model):
         Basestation('C', Point(184, 60), get_sharing_for_bs(sharing_model, 2)),
         Basestation('D', Point(97, 110), get_sharing_for_bs(sharing_model, 3)),
     ]
-
-    # UEs are now created dynamically according to CLI
-    # ue_list = [
-    #     User(str(1), map, pos_x=70, pos_y=40, movement=UniformMovement(map)),
-    #     User(str(2), map, pos_x=80, pos_y=60, movement=UniformMovement(map))
-    # ]
     return map, bs_list
 
 
-def get_env(map_size, bs_dist, num_static_ues, num_slow_ues, num_fast_ues, sharing_model, num_bs=None):
+def get_env(map_size, bs_dist, num_static_ues, num_slow_ues, num_fast_ues, sharing_model, util_func, num_bs=None):
     """Create and return the environment corresponding to the given map_size"""
     assert map_size in SUPPORTED_ENVS, f"Environment {map_size} is not one of {SUPPORTED_ENVS}."
+    assert util_func in SUPPORTED_UTILITIES, \
+            f"Utility function {util_func} not supported. Supported: {SUPPORTED_UTILITIES}"
 
     # create map and BS list
     map, bs_list = None, None
@@ -197,7 +197,7 @@ def get_env(map_size, bs_dist, num_static_ues, num_slow_ues, num_fast_ues, shari
         map, bs_list = create_custom_env(sharing_model)
 
     # create UEs
-    ue_list = create_ues(map, num_static_ues, num_slow_ues, num_fast_ues)
+    ue_list = create_ues(map, num_static_ues, num_slow_ues, num_fast_ues, util_func)
 
     return map, ue_list, bs_list
 
@@ -235,7 +235,7 @@ def create_env_config(cli_args):
     """
     env_class = get_env_class(cli_args.agent)
     map, ue_list, bs_list = get_env(cli_args.env, cli_args.bs_dist, cli_args.static_ues, cli_args.slow_ues,
-                                    cli_args.fast_ues, cli_args.sharing, cli_args.num_bs)
+                                    cli_args.fast_ues, cli_args.sharing, cli_args.util, num_bs=cli_args.num_bs)
 
     # this is for DrEnv and step utility
     # env_config = {

deepcomp/util/simulation.py

@@ -277,7 +277,7 @@ def load_agent(self, rllib_dir=None, rand_seed=None, fixed_action=1, explore=Fal
         self.agent_train_steps = self.get_training_steps()
 
     def set_result_filename(self):
-        """Return a suitable filename (without file ending) in the format 'agent_env-class_env-size_num-ues_time'"""
+        """Return a suitable filename (without file ending) depending on current configuration"""
         assert self.agent is not None, "Set the filename after loading the agent"
         timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
         agent_name = type(self.agent).__name__
@@ -293,8 +293,8 @@ def set_result_filename(self):
         test = 'rand' if self.cli_args.rand_test else 'fixed'
         seed = self.cli_args.seed
         self.result_filename = \
-            f'{agent_name}_{self.env_name}_{env_size}_{self.cli_args.sharing}_{num_ues}UEs-{self.cli_args.reward}' \
-            f'_{train}-{test}_{seed}_{timestamp}'
+            f'{agent_name}_{self.env_name}_{env_size}_{self.cli_args.sharing}_{num_ues}UEs-{self.cli_args.util}-' \
+            f'{self.cli_args.reward}_{train}-{test}_{seed}_{timestamp}'
 
     def save_animation(self, fig, patches, mode):
         """