Merge pull request #87 from UoA-CARES/dev/external_dev

Dev/external dev

cares_reinforcement_learning/util/NetworkFactory.py

@@ -1,4 +1,5 @@
 import torch
+import logging
 
 def create_DQN(args):
     from cares_reinforcement_learning.algorithm.value import DQN
@@ -159,4 +160,5 @@ def create_network(self, algorithm, args):
             return create_SAC(args)
         elif algorithm == "TD3":
             return create_TD3(args)
-        raise ValueError(f"Unkown algorithm: {algorithm}")
+        logging.warn(f"Algorithm: {algorithm} is not in the default cares_rl factory")
+        return None

cares_reinforcement_learning/util/arguement_parser.py

@@ -73,9 +73,9 @@ def algorithm_args(parent_parser):
     parser_DoubleDQN.add_argument('--exploration_min', type=float, default=1e-3)
     parser_DoubleDQN.add_argument('--exploration_decay', type=float, default=0.95)
 
-    return alg_parser
+    return alg_parser, alg_parsers
 
-def parse_args():
+def environment_parser():
     parser = argparse.ArgumentParser(add_help=False)  # Add an argument
 
     parser.add_argument('--number_training_iterations', type=int, default=1, help="Total amount of training iterations to complete")
@@ -86,7 +86,7 @@ def parse_args():
     parser.add_argument('--G', type=int, default=10, help="Number of learning updates each step of training")
     parser.add_argument('--batch_size', type=int, default=32, help="Batch Size used during training")
 
-    parser.add_argument('--max_steps_exploration', type=int, default=10000, help="Total number of steps for exploration before training")
+    parser.add_argument('--max_steps_exploration', type=int, default=1000, help="Total number of steps for exploration before training")
     parser.add_argument('--max_steps_training', type=int, default=100000, help="Total number of steps to train the algorithm")
 
     parser.add_argument('--number_steps_per_evaluation', type=int, default=10000, help="The number of steps inbetween evaluation runs during training")
@@ -97,10 +97,10 @@ def parse_args():
     parser.add_argument('--plot_frequency', type=int, default=100, help="How many steps between updating the running plot of the training and evaluation data during training")
     parser.add_argument('--checkpoint_frequency', type=int, default=100, help="How many steps between saving check point models of the agent during training")
 
-    parser = algorithm_args(parent_parser=parser)
+    return parser
+
+def create_parser():
+    parser = environment_parser()
+    parser, alg_parsers = algorithm_args(parent_parser=parser)
     parser = environment_args(parent_parser=parser)
-
-    return vars(parser.parse_args()) # converts to a dictionary
-
-if __name__ == '__main__':
-    print(parse_args())
+    return parser

example/example_training_loops.py

@@ -9,9 +9,9 @@
 from cares_reinforcement_learning.util import EnvironmentFactory
 from cares_reinforcement_learning.util import arguement_parser as ap
 
-import example.policy_example as pbe
-import example.value_example as vbe
-import ppo_example as ppe
+import cares_reinforcement_learning.train_loops.policy_loop as pbe
+import cares_reinforcement_learning.train_loops.value_loop as vbe
+import cares_reinforcement_learning.train_loops.ppo_loop as ppe
 
 import gym
 from gym import spaces
@@ -28,7 +28,8 @@ def set_seed(seed):
     random.seed(seed)
 
 def main():
-    args = ap.parse_args()
+    parser = ap.create_parser()
+    args = vars(parser.parse_args()) # converts to a dictionary
 
     args["device"] = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     logging.info(f"Device: {args['device']}")
@@ -57,6 +58,8 @@ def main():
 
         logging.info(f"Algorithm: {args['algorithm']}")
         agent = network_factory.create_network(args["algorithm"], args)
+        if agent == None:
+            raise ValueError(f"Unkown agent for default algorithms {args['algorithm']}")
 
         memory = memory_factory.create_memory(args['memory'], args)
         logging.info(f"Memory: {args['memory']}")

tests/test_utils.py

@@ -72,9 +72,9 @@ def test_create_network():
 
     agent = factory.create_network("TD3", args)
     assert isinstance(agent, TD3), "Failed to create TD3 agent"
-
-    with pytest.raises(ValueError):
-        factory.create_network("Unknown", args)
+    
+    agent = factory.create_network("Unknown", args)
+    assert agent is None, f"Unkown failed to return None: returned {agent}"
 
 
 def test_denormalize():