rename and stuff

.gitignore

+
+.idea/misc.xml

.idea/misc.xml

-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Black">
-    <option name="sdkName" value="Python 3.11 (Ion_ai1)" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (Ion_ai1)" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file

main.py

@@ -46,7 +46,7 @@ import ionen_env
 """
  Global Variables:
 """
-MODELPATH = "model.pth"
+MODELPATH = "model1.pth"
 
 # Neuer versuch mit pythorchrl
 def torchrlAlg1():
@@ -62,7 +62,7 @@ def torchrlAlg1():
 
     frames_per_batch = 1000
     # For a complete training, bring the number of frames up to 1M
-    total_frames = 1_000_000
+    total_frames = 10_000
 
     sub_batch_size = 64  # cardinality of the sub-samples gathered from the current data in the inner loop
     num_epochs = 10  # optimization steps per batch of data collected
@@ -259,12 +259,12 @@ def torchrlAlg1():
             # We evaluate the policy once every 10 batches of data.
             # Evaluation is rather simple: execute the policy without exploration
             # (take the expected value of the action distribution) for a given
-            # number of steps (1000, which is our ``env`` horizon).
+            # number of steps (1000, which is our ``env`` horizon). ****500 for me
             # The ``rollout`` method of the ``env`` can take a policy as argument:
             # it will then execute this policy at each step.
             with set_exploration_type(ExplorationType.MEAN), torch.no_grad():
                 # execute a rollout with the trained policy
-                eval_rollout = env.rollout(1000, policy_module)
+                eval_rollout = env.rollout(500, policy_module)
                 logs["eval reward"].append(eval_rollout["next", "reward"].mean().item())
                 logs["eval reward (sum)"].append(
                     eval_rollout["next", "reward"].sum().item()
@@ -282,10 +282,10 @@ def torchrlAlg1():
         # this is a nice-to-have but nothing necessary for PPO to work.
         scheduler.step()
 
-    torch.save({'policy_state_dict': policy_module.state_dict(),
-                'value_state_dict': value_module.state_dict(),
-                'loss_state_dict': loss_module.state_dict()},
-               MODELPATH)
+    # torch.save({'policy_state_dict': policy_module.state_dict(),
+    #             'value_state_dict': value_module.state_dict(),
+    #             'loss_state_dict': loss_module.state_dict()},
+    #            MODELPATH)
 
     # These modules are not good to load
     #             'collector_state_dict': collector.state_dict(),