precommit black

examples/agent_examples/whittington_2020_run.py

@@ -14,7 +14,7 @@
 from neuralplayground.experiments import Sargolini2006Data
 
 # Set the location for saving the results of the simulation
-simulation_id = "TEM_test"
+simulation_id = "TEM_begging"
 save_path = os.path.join(os.getcwd(), simulation_id)
 # save_path = os.path.join(os.getcwd(), "examples", "agent_examples", "trained_results")
 agent_class = Whittington2020

examples/agent_examples/whittington_slurm.sh

@@ -1,14 +1,14 @@
 #!/bin/bash
 
-#SBATCH -J TEM_5x5 # job name
+#SBATCH -J TEM_beg # job name
 #SBATCH -p gpu # partition (queue)
 #SBATCH -N 1   # number of nodes
 #SBATCH --mem 50G # memory pool for all cores
 #SBATCH -n 4 # number of cores
 #SBATCH -t 0-72:00 # time (D-HH:MM)
 #SBATCH --gres gpu:1 # request 1 GPU (of any kind)
-#SBATCH -o TEM_5x5.%x.%N.%j.out # STDOUT
-#SBATCH -e TEM_5x5.%x.%N.%j.err # STDERR
+#SBATCH -o TEM_beg.%x.%N.%j.out # STDOUT
+#SBATCH -e TEM_beg.%x.%N.%j.err # STDERR
 
 source ~/.bashrc
 

neuralplayground/arenas/discritized_objects.py

@@ -244,10 +244,10 @@ def step(self, action: np.ndarray, normalize_step: bool = True, skip_every: int
         }
         self.history.append(self.transition)
         self._increase_global_step()
-        self.steps_in_curr_env += 1
-        if self.steps_in_curr_env >= self.max_steps_per_env:
-            self.steps_in_curr_env = 0
-            self.reset_objects()
+        # self.steps_in_curr_env += 1
+        # if self.steps_in_curr_env >= self.max_steps_per_env:
+        #     self.steps_in_curr_env = 0
+        #     self.reset_objects()
         return observation, self.state, reward
 
     def generate_objects(self):