medium size run added

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_results_new"
+simulation_id = "TEM_results_med"
 save_path = os.path.join(os.getcwd(), simulation_id)
 # save_path = os.path.join(os.getcwd(), "examples", "agent_examples", "trained_results")
 agent_class = Whittington2020
@@ -29,7 +29,7 @@
     [-3, 3],
     [-2, 2],
     [-3, 3],
-    [-3, 3],
+    [-4, 4],
     [-2, 2],
     [-3, 3],
     [-4, 4],
@@ -65,9 +65,9 @@
 # Set parameters for the environment that generates observations
 discrete_env_params = {
     "environment_name": "DiscreteObject",
-    "state_density": 1,
+    "state_density": 2,
     "n_objects": params["n_x"],
-    "agent_step_size": 1, # Note: this must be 1 / state density
+    "agent_step_size": 1/2, # Note: this must be 1 / state density
     "use_behavioural_data": False,
     "data_path": None,
     "experiment_class": Sargolini2006Data,
@@ -106,7 +106,7 @@
 }
 
 # Full model training consists of 20000 episodes
-training_loop_params = {"n_episode": 5000, "params": full_agent_params,'random_state': False, 'custom_state': [0.0, 0.0]}
+training_loop_params = {"n_episode": 20000, "params": full_agent_params,'random_state': False, 'custom_state': [0.0, 0.0]}
 
 # Create the training simulation object
 sim = SingleSim(

examples/agent_examples/whittington_slurm.sh

@@ -1,10 +1,10 @@
 #!/bin/bash
 # Set the job name variable
-#SBATCH --job-name=TEM_small_2
+#SBATCH --job-name=TEM_med
 #SBATCH --mem=50000 # memory pool for all cores
 #SBATCH --time=72:00:00 # time
-#SBATCH -o TEM_logs/TEM_small_2.%N.%j.out # STDOUT
-#SBATCH -e TEM_logs/TEM_small_2.%N.%j.err # STDERR
+#SBATCH -o TEM_logs/TEM_med.%N.%j.out # STDOUT
+#SBATCH -e TEM_logs/TEM_med.%N.%j.err # STDERR
 #SBATCH -p gpu
 #SBATCH --gres=gpu:1
 #SBATCH --ntasks=1