Issue #10 Changed input parameters to obs and action space

s-sd · Aug 11, 2023 · 9b78c19 · 9b78c19
1 parent c987c19
commit 9b78c19
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Showing 5 changed files with 51 additions and 68 deletions.
diff --git a/demos/cartpole_single_agent.py b/demos/cartpole_single_agent.py
@@ -10,31 +10,26 @@
 tf.random.set_seed(42)
 np.random.seed(42)
 
-<<<<<<< HEAD
-=======
-def build_policy_network(state_shape, num_actions):
-    inputs = tf.keras.layers.Input(shape=state_shape)
-    flat = tf.keras.layers.Flatten()(inputs)
-    dense1 = tf.keras.layers.Dense(64, activation='relu')(flat)
-    dropout1 = tf.keras.layers.Dropout(0.4)(dense1)
-    dense2 = tf.keras.layers.Dense(32, activation='relu')(dropout1)
-    dropout2 = tf.keras.layers.Dropout(0.4)(dense2)
-    dense3 = tf.keras.layers.Dense(14, activation='relu')(dropout2)     
-    dense4 = tf.keras.layers.Dense(num_actions, activation='softmax')(dense3)
-    policy_network = tf.keras.Model(inputs=inputs, outputs=dense4)
-    return policy_network
-
->>>>>>> main
+# def build_policy_network(state_shape, num_actions):
+#     inputs = tf.keras.layers.Input(shape=state_shape)
+#     flat = tf.keras.layers.Flatten()(inputs)
+#     dense1 = tf.keras.layers.Dense(64, activation='relu')(flat)
+#     dropout1 = tf.keras.layers.Dropout(0.4)(dense1)
+#     dense2 = tf.keras.layers.Dense(32, activation='relu')(dropout1)
+#     dropout2 = tf.keras.layers.Dropout(0.4)(dense2)
+#     dense3 = tf.keras.layers.Dense(14, activation='relu')(dropout2)     
+#     dense4 = tf.keras.layers.Dense(num_actions, activation='softmax')(dense3)
+#     policy_network = tf.keras.Model(inputs=inputs, outputs=dense4)
+#     return policy_network
+
 env = gym.make('CartPole-v1')
 
-state_shape = env.observation_space.shape
+state_space = env.observation_space
 action_space = env.action_space
-num_actions = action_space.n
 
 # Build policy network 
-policy_network = build_policy_network(state_shape, 
-                                      action_size = num_actions, 
-                                      action_space = action_space, 
+policy_network = build_policy_network(state_space, 
+                                      action_space, 
                                       policy_type = 'fcn',
                                       layers = [64, 32, 14])
 

diff --git a/demos/dummy_single_agent_continuous.py b/demos/dummy_single_agent_continuous.py
@@ -39,11 +39,10 @@ def reset(self):
 env = DummyEnv(action_size)
 
 action_space = env.action_space
-state_shape = env.observation_space.shape
+state_space = env.observation_space
 
-policy_network = build_policy_network(state_shape, 
-                                      action_size = action_size, 
-                                      action_space = action_space,
+policy_network = build_policy_network(state_space, 
+                                      action_space,
                                       policy_type = 'fcn',
                                       layers = [128],
                                       activation_fn = 'linear')

diff --git a/demos/dummy_single_agent_discrete.py b/demos/dummy_single_agent_discrete.py
@@ -38,11 +38,10 @@ def reset(self):
 env = DummyEnv(num_actions)
 
 # Building policy network
-state_shape = env.observation_space.shape
+state_space = env.observation_space
 action_space = env.action_space
-policy_network = build_policy_network(state_shape,
-                                      action_size = num_actions,
-                                      action_space = action_space,
+policy_network = build_policy_network(state_space,
+                                      action_space,
                                       policy_type = 'fcn',
                                       layers = [128])
 

diff --git a/demos/pendulum_single_agent.py b/demos/pendulum_single_agent.py
@@ -10,33 +10,28 @@
 tf.random.set_seed(42)
 np.random.seed(42)
 
-<<<<<<< HEAD
-=======
-def build_policy_network(state_shape, action_size):
-    inputs = tf.keras.layers.Input(shape=state_shape)
-    flat = tf.keras.layers.Flatten()(inputs)
-    dense1 = tf.keras.layers.Dense(128, activation='relu')(flat)
-    dropout1 = tf.keras.layers.Dropout(0.4)(dense1)
-    dense2 = tf.keras.layers.Dense(64, activation='relu')(dropout1)
-    dropout2 = tf.keras.layers.Dropout(0.4)(dense2)
-    dense3 = tf.keras.layers.Dense(32, activation='relu')(dropout2)    
-    dense4 = tf.keras.layers.Dense(np.prod(action_size), activation='tanh')(dense3)
+# def build_policy_network(state_shape, action_size):
+#     inputs = tf.keras.layers.Input(shape=state_shape)
+#     flat = tf.keras.layers.Flatten()(inputs)
+#     dense1 = tf.keras.layers.Dense(128, activation='relu')(flat)
+#     dropout1 = tf.keras.layers.Dropout(0.4)(dense1)
+#     dense2 = tf.keras.layers.Dense(64, activation='relu')(dropout1)
+#     dropout2 = tf.keras.layers.Dropout(0.4)(dense2)
+#     dense3 = tf.keras.layers.Dense(32, activation='relu')(dropout2)    
+#     dense4 = tf.keras.layers.Dense(np.prod(action_size), activation='tanh')(dense3)
 
-    scaled_outputs = tf.keras.layers.Lambda(lambda x: (x + 1) * 2 - 2)(dense4) # scale to action space
+#     scaled_outputs = tf.keras.layers.Lambda(lambda x: (x + 1) * 2 - 2)(dense4) # scale to action space
 
-    policy_network = tf.keras.Model(inputs=inputs, outputs=scaled_outputs)
-    return policy_network
+#     policy_network = tf.keras.Model(inputs=inputs, outputs=scaled_outputs)
+#     return policy_network
 
->>>>>>> main
 env = gym.make("Pendulum-v1")
 
 action_space = env.action_space
-action_size = env.action_space.shape
-state_shape = env.observation_space.shape
+state_space = env.observation_space
 
-policy_network = build_policy_network(state_shape, 
-                                      action_size = action_size, 
-                                      action_space = action_space,
+policy_network = build_policy_network(state_space, 
+                                      action_space,
                                       policy_type = 'fcn',
                                       layers = [128, 64, 32],
                                       activation_fn = 'tanh')

diff --git a/demos/tictactoe_sequential_selfplay.py b/demos/tictactoe_sequential_selfplay.py
@@ -18,21 +18,18 @@
 tf.random.set_seed(42)
 np.random.seed(42)
 
-<<<<<<< HEAD
-=======
-def build_policy_network(state_shape, action_size):
-    inputs = tf.keras.layers.Input(shape=state_shape)
-    flat = tf.keras.layers.Flatten()(inputs)
-    dense1 = tf.keras.layers.Dense(128, activation='relu')(flat)
-    dropout1 = tf.keras.layers.Dropout(0.4)(dense1)
-    dense2 = tf.keras.layers.Dense(64, activation='relu')(dropout1);
-    dropout2 = tf.keras.layers.Dropout(0.4)(dense2)
-    dense3 = tf.keras.layers.Dense(32, activation='relu')(dropout2)    
-    dense4 = tf.keras.layers.Dense(np.prod(action_size), activation='softmax')(dense3)
-    policy_network = tf.keras.Model(inputs=inputs, outputs=dense4)
-    return policy_network
-
->>>>>>> main
+# def build_policy_network(state_shape, action_size):
+#     inputs = tf.keras.layers.Input(shape=state_shape)
+#     flat = tf.keras.layers.Flatten()(inputs)
+#     dense1 = tf.keras.layers.Dense(128, activation='relu')(flat)
+#     dropout1 = tf.keras.layers.Dropout(0.4)(dense1)
+#     dense2 = tf.keras.layers.Dense(64, activation='relu')(dropout1);
+#     dropout2 = tf.keras.layers.Dropout(0.4)(dense2)
+#     dense3 = tf.keras.layers.Dense(32, activation='relu')(dropout2)    
+#     dense4 = tf.keras.layers.Dense(np.prod(action_size), activation='softmax')(dense3)
+#     policy_network = tf.keras.Model(inputs=inputs, outputs=dense4)
+#     return policy_network
+
 env = TicTacToeEnv()
 
 class REINFORCE_TicTacToe(REINFORCE):
@@ -43,13 +40,11 @@ def invert_state(self, state):
         state[:, :, 0] *= -1
         return state
 
-state_shape = env.observation_space.shape
+state_space = env.observation_space
 action_space = env.action_space
-num_actions = (action_space.n,)
 
-policy_network = build_policy_network(state_shape,
-                                      action_size = num_actions,
-                                      action_space = action_space,
+policy_network = build_policy_network(state_space,
+                                      action_space,
                                       policy_type = 'fcn',
                                       layers = [128, 64, 32])