From 3b9051505ef5a8fe7c23190ef4326dce4e6e44d8 Mon Sep 17 00:00:00 2001
From: Arec Jamgochian <jamgochian95@gmail.com>
Date: Sat, 26 Feb 2022 16:02:29 -0800
Subject: [PATCH] naming convention doesnt like env key, leakyrelu is no bueno,
 running quick experiment to see which option sets can work with our time
 left. the options that look 4s ahead are definitely out of the questions,
 possibly the 2s ones aswell

---
 sgail-ppo-options-setobs2.py | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/sgail-ppo-options-setobs2.py b/sgail-ppo-options-setobs2.py
index 09eca49..6809545 100644
--- a/sgail-ppo-options-setobs2.py
+++ b/sgail-ppo-options-setobs2.py
@@ -21,11 +21,10 @@ import json
 
 DIR = os.path.dirname(os.path.abspath(__file__))
 option_list = [[(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5]],
-                [(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20]],
-                [(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5, 10, 20]], # was the best in training with single hidden layer, but very slow
-                [(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20, 40]], 
-                [(vel, time) for vel in [0, 2, 5, 10] for time in [5, 10, 20]],
-                [(vel, time) for vel in [0, 3, 10] for time in [5, 20, 40]]
+                [(vel, time) for vel in [0, 2.5, 5, 7.5, 10] for time in [5, 10]],
+                [(vel, time) for vel in [0, 2.5, 5, 7.5, 10] for time in [5, 20]],
+                [(vel, time) for vel in [0, 2.5, 5, 10] for time in [5, 10, 20]],
+                [(vel, time) for vel in [0, 3, 10] for time in [5, 10, 20]]
 ]
 
 obs_min = np.array([
@@ -58,11 +57,12 @@ def training_function(config):
                 collision_penalty=0
             ),
             check_collisions=True,
-            stop_on_collision=config['env']['stop_on_collision'], track=track,
+            stop_on_collision=config['trainenv']['stop_on_collision'], track=track,
         ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
-    ), options=option_list[config['policy']['option']],
-        safe_actions_collision_method=config['env']['safe_actions_collision_method'], 
-        abort_unsafe_collision_method=config['env']['abort_unsafe_collision_method']) for _ in range(20)] for track in range(4)],[])
+        ), options=option_list[config['policy']['option']],
+        safe_actions_collision_method=config['trainenv']['safe_actions_collision_method'], 
+        abort_unsafe_collision_method=config['trainenv']['abort_unsafe_collision_method'],
+        ) for _ in range(20)] for track in range(4)],[])
 
     env_fn = lambda i: envs[i]
 
@@ -118,9 +118,9 @@ def training_function(config):
         value=value,
         v_opt=v_opt,
         v_iters=config['value']['iterations_per_epoch'],
-        epochs=301,
+        epochs=20, #300, #200 FIXME
         rollout_episodes=60,
-        rollout_steps=60,
+        rollout_steps=60, 
         gamma=0.99,
         gae_lambda=0.9,
         clip_ratio=config['policy']['clip_ratio'],
@@ -137,19 +137,19 @@ def training_function(config):
 analysis = tune.run(
     training_function,
     config={
-        'env': {
-            'stop_on_collision': False,
-            'safe_actions_collision_method': 'circle',
-            'abort_unsafe_collision_method': 'circle',
+        'trainenv': {
+            'stop_on_collision': False, #tune.grid_search([False, True]),
+            'safe_actions_collision_method': 'circle', 
+            'abort_unsafe_collision_method': 'circle', 
         },
         'policy': {
             'learning_rate': 3e-4, # tune.grid_search([3e-4]),
             'learning_rate_decay': 1.0, #tune.grid_search([1.0]),
             'clip_ratio': 0.2, #tune.grid_search([0.2]),
             'iterations_per_epoch': 100, #tune.grid_search([100]),
-            'hidden_layer_size': tune.grid_search([10, 20, 40]),
-            'n_hidden_layers': tune.grid_search([2, 3, 4]),
-            'activation':tune.grid_search([torch.nn.LeakyReLU, torch.nn.Tanh]),
+            'hidden_layer_size': 10, #tune.grid_search([10, 20, 40]), FIXME
+            'n_hidden_layers': 2, #tune.grid_search([2, 3, 4]), FIXME
+            'activation':torch.nn.Tanh,
             'option': tune.grid_search(list(range(len(option_list))))
         },
         'value': {