{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "nwaAZRu1NTiI"
},
"source": [
"# DQN v2\n",
"\n",
"#### This version implements DQN with Keras\n",
"#### Findings:\n",
"Smaller NET then v1 does not work"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"id": "LNXxxKojNTiL"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.utils import to_categorical\n",
"import gym\n",
"from gym import spaces\n",
"from gym.utils import seeding\n",
"from gym import wrappers\n",
"\n",
"from tqdm.notebook import tqdm\n",
"from collections import deque\n",
"import numpy as np\n",
"import random\n",
"from matplotlib import pyplot as plt\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"import joblib\n",
"\n",
"import io\n",
"import base64\n",
"from IPython.display import HTML, Video\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"class DQN:\n",
" def __init__(self, env=None, replay_buffer_size=1000, action_size=2):\n",
" self.replay_buffer = deque(maxlen=replay_buffer_size)\n",
"\n",
" self.action_size = action_size\n",
"\n",
" # Hyperparameters\n",
" self.gamma = 0.95 # Discount rate\n",
" self.epsilon = 1.0 # Exploration rate\n",
" self.epsilon_min = 0.001 # Minimal exploration rate (epsilon-greedy)\n",
" self.epsilon_decay = 0.95 # Decay rate for epsilon\n",
" self.update_rate = 5 # Number of steps until updating the target network\n",
" self.batch_size = 100\n",
" self.learning_rate = 5e-4\n",
" \n",
" # Construct DQN models\n",
" self.model = self._build_model()\n",
" self.target_model = self._build_model()\n",
" self.target_model.set_weights(self.model.get_weights())\n",
" self.model.summary()\n",
" self.env = env\n",
" self.action_size = action_size\n",
"\n",
" self.scaler = None\n",
"\n",
" def _build_model(self):\n",
" model = tf.keras.Sequential()\n",
" \n",
" model.add(tf.keras.Input(shape=(4,)))\n",
" model.add(layers.Dense(512, activation = 'relu'))\n",
" model.add(layers.Dense(256, activation = 'relu'))\n",
" model.add(layers.Dense(128, activation = 'relu'))\n",
" model.add(layers.Dense(self.action_size, activation = 'linear'))\n",
" # model.compile(optimizer = RMSprop(lr = self.lr, rho = 0.95, epsilon = 0.01), loss = \"mse\", metrics = ['accuracy'])\n",
" \n",
" optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)\n",
" # model.compile(loss='mse', optimizer=tf.keras.optimizers.RMSprop(lr = self.learning_rate, rho = 0.95, epsilon = 0.01), metrics = ['accuracy'])\n",
" model.compile(loss='mse', optimizer=optimizer, metrics = ['accuracy'])\n",
" return model\n",
"\n",
" def _min_max(self):\n",
" \"\"\"Run some steps to get data to do MINMAX scale \"\"\"\n",
" state_arr = []\n",
" state_arr.append(self.env.observation_space.high)\n",
" state_arr[0][1], state_arr[0][3] = 0,0\n",
" state_arr.append(self.env.observation_space.low)\n",
" state_arr[1][1], state_arr[1][3] = 0,0\n",
" state = self.env.reset()\n",
" for i in range(1000):\n",
" random_action = self.env.action_space.sample()\n",
" next_state, reward, done, info = self.env.step(random_action)\n",
" state_arr.append(next_state)\n",
" if done:\n",
" state = self.env.reset()\n",
"\n",
" state_arr = np.array(state_arr)\n",
" self.scaler = MinMaxScaler()\n",
" self.scaler.fit(state_arr)\n",
" # print(self.scaler.data_max_)\n",
" # print(self.scaler.data_min_)\n",
"\n",
" def _get_scaled_state(self, state):\n",
" return state\n",
" # return self.scaler.transform(state.reshape(1,-1)).flatten()\n",
"\n",
" #\n",
" # Trains the model using randomly selected experiences in the replay memory\n",
" #\n",
" def _train(self):\n",
" X, y = [], []\n",
" # state, action, reward, next_state, done \n",
" # create the targets \n",
" if self.batch_size > len(self.replay_buffer):\n",
" return\n",
" minibatch = random.sample(self.replay_buffer, self.batch_size)\n",
" mb_arr = np.array(minibatch, dtype=object)\n",
"\n",
" next_state_arr = np.stack(mb_arr[:,3])\n",
" future_qvalues = self.target_model.predict(next_state_arr, verbose=0)\n",
"\n",
" state_arr = np.stack(mb_arr[:,0])\n",
" qvalues = self.model.predict(state_arr, verbose=0)\n",
"\n",
" for index, (state, action, reward, next_state, done) in enumerate(minibatch):\n",
" if done == True:\n",
" q_target = reward\n",
" else:\n",
" q_target = reward + self.gamma * np.max(future_qvalues[index])\n",
"\n",
" q_curr = qvalues[index]\n",
" q_curr[action] = q_target \n",
" X.append(state)\n",
" y.append(q_curr)\n",
"\n",
" # Perform gradient step\n",
" X, y = np.array(X), np.array(y)\n",
" history = self.model.fit(X, y, batch_size = self.batch_size, shuffle = False, verbose=0)\n",
" # history = self.model.fit(X, y, epochs=1, verbose=0)\n",
" # print(f\"Loss: {history.history['loss']} \")\n",
"\n",
"\n",
" def learn(self, total_steps=None):\n",
" #create scaler\n",
" self._min_max()\n",
" current_episode = 0\n",
" total_reward = 0\n",
" rewards = [0]\n",
" current_step = 0\n",
" while current_step < total_steps:\n",
" current_episode += 1\n",
" state = self.env.reset()\n",
" state = self._get_scaled_state(state)\n",
" total_reward = 0\n",
" done = False\n",
" while done != True:\n",
" current_step +=1\n",
" # e-greedy\n",
" if np.random.random() > (1 - self.epsilon):\n",
" action = random.randrange(self.action_size)\n",
" else:\n",
" model_predict = self.model.predict(np.array([state]), verbose=0)\n",
" action = np.argmax(model_predict)\n",
"\n",
" # step\n",
" next_state, reward, done, info = self.env.step(action)\n",
" total_reward += reward\n",
"\n",
" next_state = self._get_scaled_state(next_state)\n",
"\n",
" # add to buffer\n",
" self.replay_buffer.append((state, action, reward, next_state, done))\n",
"\n",
" if current_step>10 and current_step % self.update_rate == 0:\n",
" print(f\"epsilon:{self.epsilon} step:{current_step} episode:{current_episode} last_score {rewards[-1]} \")\n",
" self._train()\n",
" # update target\n",
" self.target_model.set_weights(self.model.get_weights())\n",
" \n",
" state = next_state\n",
"\n",
" # update epsilon every 100 steps \n",
" if current_step % 20 == 0:\n",
" if self.epsilon > self.epsilon_min:\n",
" self.epsilon *= self.epsilon_decay\n",
"\n",
" rewards.append(total_reward)\n",
"\n",
" #\n",
" # Loads a saved model\n",
" #\n",
" def load(self, name):\n",
" self.model.load_weights(name)\n",
" self.scaler = joblib.load(name+\".scaler\") \n",
"\n",
" #\n",
" # Saves parameters of a trained model\n",
" #\n",
" def save(self, name):\n",
" self.model.save_weights(name)\n",
" joblib.dump(self.scaler, name+\".scaler\") \n",
"\n",
" def play(self, state):\n",
" state = self._get_scaled_state(state)\n",
" return np.argmax(self.model.predict(np.array([state]), verbose=0)[0])"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_36\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_144 (Dense) (None, 512) 2560 \n",
" \n",
" dense_145 (Dense) (None, 256) 131328 \n",
" \n",
" dense_146 (Dense) (None, 128) 32896 \n",
" \n",
" dense_147 (Dense) (None, 2) 258 \n",
" \n",
"=================================================================\n",
"Total params: 167,042\n",
"Trainable params: 167,042\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"epsilon:1.0 step:15 episode:1 last_score 0 \n",
"epsilon:1.0 step:20 episode:1 last_score 0 \n",
"epsilon:0.95 step:25 episode:1 last_score 0 \n",
"epsilon:0.95 step:30 episode:2 last_score 26.0 \n",
"epsilon:0.95 step:35 episode:2 last_score 26.0 \n",
"epsilon:0.95 step:40 episode:3 last_score 11.0 \n",
"epsilon:0.9025 step:45 episode:3 last_score 11.0 \n",
"epsilon:0.9025 step:50 episode:3 last_score 11.0 \n",
"epsilon:0.9025 step:55 episode:3 last_score 11.0 \n",
"epsilon:0.9025 step:60 episode:4 last_score 21.0 \n",
"epsilon:0.8573749999999999 step:65 episode:4 last_score 21.0 \n",
"epsilon:0.8573749999999999 step:70 episode:4 last_score 21.0 \n",
"epsilon:0.8573749999999999 step:75 episode:5 last_score 13.0 \n",
"epsilon:0.8573749999999999 step:80 episode:5 last_score 13.0 \n",
"epsilon:0.8145062499999999 step:85 episode:5 last_score 13.0 \n",
"epsilon:0.8145062499999999 step:90 episode:5 last_score 13.0 \n",
"epsilon:0.8145062499999999 step:95 episode:6 last_score 21.0 \n",
"epsilon:0.8145062499999999 step:100 episode:6 last_score 21.0 \n",
"epsilon:0.7737809374999999 step:105 episode:7 last_score 11.0 \n",
"epsilon:0.7737809374999999 step:110 episode:7 last_score 11.0 \n",
"epsilon:0.7737809374999999 step:115 episode:7 last_score 11.0 \n",
"epsilon:0.7737809374999999 step:120 episode:8 last_score 13.0 \n",
"epsilon:0.7350918906249998 step:125 episode:8 last_score 13.0 \n",
"epsilon:0.7350918906249998 step:130 episode:8 last_score 13.0 \n",
"epsilon:0.7350918906249998 step:135 episode:8 last_score 13.0 \n",
"epsilon:0.7350918906249998 step:140 episode:8 last_score 13.0 \n",
"epsilon:0.6983372960937497 step:145 episode:8 last_score 13.0 \n",
"epsilon:0.6983372960937497 step:150 episode:8 last_score 13.0 \n",
"epsilon:0.6983372960937497 step:155 episode:8 last_score 13.0 \n",
"epsilon:0.6983372960937497 step:160 episode:9 last_score 40.0 \n",
"epsilon:0.6634204312890623 step:165 episode:9 last_score 40.0 \n",
"epsilon:0.6634204312890623 step:170 episode:9 last_score 40.0 \n",
"epsilon:0.6634204312890623 step:175 episode:10 last_score 15.0 \n",
"epsilon:0.6634204312890623 step:180 episode:10 last_score 15.0 \n",
"epsilon:0.6302494097246091 step:185 episode:10 last_score 15.0 \n",
"epsilon:0.6302494097246091 step:190 episode:10 last_score 15.0 \n",
"epsilon:0.6302494097246091 step:195 episode:10 last_score 15.0 \n",
"epsilon:0.6302494097246091 step:200 episode:11 last_score 27.0 \n",
"epsilon:0.5987369392383786 step:205 episode:11 last_score 27.0 \n",
"epsilon:0.5987369392383786 step:210 episode:12 last_score 8.0 \n",
"epsilon:0.5987369392383786 step:215 episode:12 last_score 8.0 \n",
"epsilon:0.5987369392383786 step:220 episode:13 last_score 12.0 \n",
"epsilon:0.5688000922764596 step:225 episode:13 last_score 12.0 \n",
"epsilon:0.5688000922764596 step:230 episode:13 last_score 12.0 \n",
"epsilon:0.5688000922764596 step:235 episode:14 last_score 13.0 \n",
"epsilon:0.5688000922764596 step:240 episode:14 last_score 13.0 \n",
"epsilon:0.5403600876626365 step:245 episode:15 last_score 12.0 \n",
"epsilon:0.5403600876626365 step:250 episode:15 last_score 12.0 \n",
"epsilon:0.5403600876626365 step:255 episode:15 last_score 12.0 \n",
"epsilon:0.5403600876626365 step:260 episode:16 last_score 15.0 \n",
"epsilon:0.5133420832795047 step:265 episode:16 last_score 15.0 \n",
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"epsilon:0.48767497911552943 step:285 episode:17 last_score 23.0 \n",
"epsilon:0.48767497911552943 step:290 episode:17 last_score 23.0 \n",
"epsilon:0.48767497911552943 step:295 episode:18 last_score 9.0 \n",
"epsilon:0.48767497911552943 step:300 episode:19 last_score 9.0 \n",
"epsilon:0.46329123015975293 step:305 episode:19 last_score 9.0 \n",
"epsilon:0.46329123015975293 step:310 episode:19 last_score 9.0 \n",
"epsilon:0.46329123015975293 step:315 episode:20 last_score 14.0 \n",
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"epsilon:0.44012666865176525 step:325 episode:20 last_score 14.0 \n",
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"epsilon:0.44012666865176525 step:335 episode:21 last_score 21.0 \n",
"epsilon:0.44012666865176525 step:340 episode:21 last_score 21.0 \n",
"epsilon:0.41812033521917696 step:345 episode:21 last_score 21.0 \n",
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"epsilon:0.41812033521917696 step:360 episode:23 last_score 9.0 \n",
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]
}
],
"source": [
"env = gym.make('CartPole-v1')\n",
"\n",
"model = DQN(env=env, replay_buffer_size=10_000, action_size=2)\n",
"model.learn(total_steps=6_000)\n",
"env.close()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"model.save(\"./alt/DQN_v2.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_38\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_152 (Dense) (None, 512) 2560 \n",
" \n",
" dense_153 (Dense) (None, 256) 131328 \n",
" \n",
" dense_154 (Dense) (None, 128) 32896 \n",
" \n",
" dense_155 (Dense) (None, 2) 258 \n",
" \n",
"=================================================================\n",
"Total params: 167,042\n",
"Trainable params: 167,042\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"Total reward 439.0\n"
]
}
],
"source": [
"eval_env = gym.make('CartPole-v1')\n",
"model = DQN(env=eval_env, replay_buffer_size=10_000, action_size=2)\n",
"model.load(\"./alt/DQN_v2.h5\")\n",
"eval_env = wrappers.Monitor(eval_env, \"./alt/gym-results\", force=True)\n",
"state = eval_env.reset()\n",
"total_reward = 0\n",
"for _ in range(1000):\n",
" action = model.play(state)\n",
" observation, reward, done, info = eval_env.step(action)\n",
" total_reward +=reward\n",
" state = observation\n",
" if done: \n",
" break\n",
"print(f\"Total reward {total_reward}\")\n",
"eval_env.close()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 4.8000002e+00 0.0000000e+00 4.1887903e-01 0.0000000e+00]\n",
" [-4.8000002e+00 0.0000000e+00 -4.1887903e-01 0.0000000e+00]\n",
" [-4.5489166e-03 -2.4116948e-01 -4.1077949e-02 3.0230245e-01]\n",
" ...\n",
" [ 5.0607350e-02 1.7593256e-01 -8.8660561e-02 -4.2944443e-01]\n",
" [ 5.4126002e-02 3.7219086e-01 -9.7249448e-02 -7.4870765e-01]\n",
" [ 6.1569817e-02 5.6851023e-01 -1.1222360e-01 -1.0703416e+00]]\n",
"Max [4.8 2.2215695 0.41887903 2.1663814 ]\n",
"Min [-4.8 -1.4063605 -0.41887903 -2.5751066 ]\n",
"[ 0.0172858 0.00332159 -0.03439883 0.01944862]\n",
"[0.5018006 0.38856372 0.45893943 0.54720277]\n"
]
}
],
"source": [
"\n",
"# def _min_max(env):\n",
"# \"\"\"Run some steps to get data to do MINMAX scale \"\"\"\n",
"# state_arr = []\n",
"# state_arr.append(env.observation_space.high)\n",
"# state_arr[0][1], state_arr[0][3] = 0,0\n",
"# state_arr.append(env.observation_space.low)\n",
"# state_arr[1][1], state_arr[1][3] = 0,0\n",
"# state = env.reset()\n",
"# for i in range(1000):\n",
"# random_action = env.action_space.sample()\n",
"# next_state, reward, done, info = env.step(random_action)\n",
"# state_arr.append(next_state)\n",
"# if done:\n",
"# state = env.reset()\n",
"\n",
"# state_arr = np.array(state_arr)\n",
"\n",
"# print(state_arr)\n",
"# scaler = MinMaxScaler()\n",
"# scaler.fit(state_arr)\n",
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"# state = env.reset()\n",
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"# print(state)\n"
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