Tested with TF 2.3.1

pythonlessons · web-flow · commit 4016a9c55f3f · 2020-10-07T20:34:40.000+03:00
diff --git a/05_CartPole-reinforcement-learning_PER_D3QN/Cartpole_PER_D3QN_TF2.py b/05_CartPole-reinforcement-learning_PER_D3QN/Cartpole_PER_D3QN_TF2.py
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+# Tutorial by www.pylessons.com
+# Tutorial written for - Tensorflow 2.3.1
+
+import os
+import random
+import gym
+import pylab
+import numpy as np
+from collections import deque
+from tensorflow.keras.models import Model, load_model
+from tensorflow.keras.layers import Input, Dense, Lambda, Add
+from tensorflow.keras.optimizers import Adam, RMSprop
+from tensorflow.keras import backend as K
+from PER import *
+
+def OurModel(input_shape, action_space, dueling):
+    X_input = Input(input_shape)
+    X = X_input
+
+    # 'Dense' is the basic form of a neural network layer
+    # Input Layer of state size(4) and Hidden Layer with 512 nodes
+    X = Dense(512, input_shape=input_shape, activation="relu", kernel_initializer='he_uniform')(X)
+
+    # Hidden layer with 256 nodes
+    X = Dense(256, activation="relu", kernel_initializer='he_uniform')(X)
+    
+    # Hidden layer with 64 nodes
+    X = Dense(64, activation="relu", kernel_initializer='he_uniform')(X)
+
+    if dueling:
+        state_value = Dense(1, kernel_initializer='he_uniform')(X)
+        state_value = Lambda(lambda s: K.expand_dims(s[:, 0], -1), output_shape=(action_space,))(state_value)
+
+        action_advantage = Dense(action_space, kernel_initializer='he_uniform')(X)
+        action_advantage = Lambda(lambda a: a[:, :] - K.mean(a[:, :], keepdims=True), output_shape=(action_space,))(action_advantage)
+
+        X = Add()([state_value, action_advantage])
+    else:
+        # Output Layer with # of actions: 2 nodes (left, right)
+        X = Dense(action_space, activation="linear", kernel_initializer='he_uniform')(X)
+
+    model = Model(inputs = X_input, outputs = X)
+    model.compile(loss="mean_squared_error", optimizer=RMSprop(lr=0.00025, rho=0.95, epsilon=0.01), metrics=["accuracy"])
+
+    model.summary()
+    return model
+
+class DQNAgent:
+    def __init__(self, env_name):
+        self.env_name = env_name       
+        self.env = gym.make(env_name)
+        self.env.seed(0)  
+        # by default, CartPole-v1 has max episode steps = 500
+        self.env._max_episode_steps = 4000
+        self.state_size = self.env.observation_space.shape[0]
+        self.action_size = self.env.action_space.n
+
+        self.EPISODES = 1000
+        memory_size = 10000
+        self.MEMORY = Memory(memory_size)
+        self.memory = deque(maxlen=2000)
+        self.gamma = 0.95    # discount rate
+
+        # EXPLORATION HYPERPARAMETERS for epsilon and epsilon greedy strategy
+        self.epsilon = 1.0 # exploration probability at start
+        self.epsilon_min = 0.01 # minimum exploration probability
+        self.epsilon_decay = 0.0005 # exponential decay rate for exploration prob
+        
+        self.batch_size = 32
+
+        # defining model parameters
+        self.ddqn = True # use doudle deep q network
+        self.Soft_Update = False # use soft parameter update
+        self.dueling = True # use dealing netowrk
+        self.epsilot_greedy = False # use epsilon greedy strategy
+        self.USE_PER = True
+
+        self.TAU = 0.1 # target network soft update hyperparameter
+
+        self.Save_Path = 'Models'
+        if not os.path.exists(self.Save_Path): os.makedirs(self.Save_Path)
+        self.scores, self.episodes, self.average = [], [], []
+
+        self.Model_name = os.path.join(self.Save_Path, self.env_name+"_e_greedy.h5")
+        
+        # create main model and target model
+        self.model = OurModel(input_shape=(self.state_size,), action_space = self.action_size, dueling = self.dueling)
+        self.target_model = OurModel(input_shape=(self.state_size,), action_space = self.action_size, dueling = self.dueling)
+
+    # after some time interval update the target model to be same with model
+    def update_target_model(self):
+        if not self.Soft_Update and self.ddqn:
+            self.target_model.set_weights(self.model.get_weights())
+            return
+        if self.Soft_Update and self.ddqn:
+            q_model_theta = self.model.get_weights()
+            target_model_theta = self.target_model.get_weights()
+            counter = 0
+            for q_weight, target_weight in zip(q_model_theta, target_model_theta):
+                target_weight = target_weight * (1-self.TAU) + q_weight * self.TAU
+                target_model_theta[counter] = target_weight
+                counter += 1
+            self.target_model.set_weights(target_model_theta)
+
+    def remember(self, state, action, reward, next_state, done):
+        experience = state, action, reward, next_state, done
+        if self.USE_PER:
+            self.MEMORY.store(experience)
+        else:
+            self.memory.append((experience))
+
+    def act(self, state, decay_step):
+        # EPSILON GREEDY STRATEGY
+        if self.epsilot_greedy:
+        # Here we'll use an improved version of our epsilon greedy strategy for Q-learning
+            explore_probability = self.epsilon_min + (self.epsilon - self.epsilon_min) * np.exp(-self.epsilon_decay * decay_step)
+        # OLD EPSILON STRATEGY
+        else:
+            if self.epsilon > self.epsilon_min:
+                self.epsilon *= (1-self.epsilon_decay)
+            explore_probability = self.epsilon
+    
+        if explore_probability > np.random.rand():
+            # Make a random action (exploration)
+            return random.randrange(self.action_size), explore_probability
+        else:
+            # Get action from Q-network (exploitation)
+            # Estimate the Qs values state
+            # Take the biggest Q value (= the best action)
+            return np.argmax(self.model.predict(state)), explore_probability
+
+    def replay(self):
+        if self.USE_PER:
+            tree_idx, minibatch = self.MEMORY.sample(self.batch_size)
+        else:
+            minibatch = random.sample(self.memory, min(len(self.memory), self.batch_size))
+
+        state = np.zeros((self.batch_size, self.state_size))
+        next_state = np.zeros((self.batch_size, self.state_size))
+        action, reward, done = [], [], []
+
+        # do this before prediction
+        # for speedup, this could be done on the tensor level
+        # but easier to understand using a loop
+        for i in range(self.batch_size):
+            state[i] = minibatch[i][0]
+            action.append(minibatch[i][1])
+            reward.append(minibatch[i][2])
+            next_state[i] = minibatch[i][3]
+            done.append(minibatch[i][4])
+
+        # do batch prediction to save speed
+        # predict Q-values for starting state using the main network
+        target = self.model.predict(state)
+        target_old = np.array(target)
+        # predict best action in ending state using the main network
+        target_next = self.model.predict(next_state)
+        # predict Q-values for ending state using the target network
+        target_val = self.target_model.predict(next_state)
+
+        for i in range(len(minibatch)):
+            # correction on the Q value for the action used
+            if done[i]:
+                target[i][action[i]] = reward[i]
+            else:
+                if self.ddqn: # Double - DQN
+                    # current Q Network selects the action
+                    # a'_max = argmax_a' Q(s', a')
+                    a = np.argmax(target_next[i])
+                    # target Q Network evaluates the action
+                    # Q_max = Q_target(s', a'_max)
+                    target[i][action[i]] = reward[i] + self.gamma * (target_val[i][a])   
+                else: # Standard - DQN
+                    # DQN chooses the max Q value among next actions
+                    # selection and evaluation of action is on the target Q Network
+                    # Q_max = max_a' Q_target(s', a')
+                    target[i][action[i]] = reward[i] + self.gamma * (np.amax(target_next[i]))
+
+        if self.USE_PER:
+            indices = np.arange(self.batch_size, dtype=np.int32)
+            absolute_errors = np.abs(target_old[indices, np.array(action)]-target[indices, np.array(action)])
+            # Update priority
+            self.MEMORY.batch_update(tree_idx, absolute_errors)
+
+        # Train the Neural Network with batches
+        self.model.fit(state, target, batch_size=self.batch_size, verbose=0)
+
+    def load(self, name):
+        self.model = load_model(name)
+
+    def save(self, name):
+        self.model.save(name)
+
+    pylab.figure(figsize=(18, 9))
+    def PlotModel(self, score, episode):
+        self.scores.append(score)
+        self.episodes.append(episode)
+        self.average.append(sum(self.scores[-50:]) / len(self.scores[-50:]))
+        pylab.plot(self.episodes, self.average, 'r')
+        pylab.plot(self.episodes, self.scores, 'b')
+        pylab.ylabel('Score', fontsize=18)
+        pylab.xlabel('Steps', fontsize=18)
+        dqn = 'DQN_'
+        softupdate = ''
+        dueling = ''
+        greedy = ''
+        PER = ''
+        if self.ddqn: dqn = 'DDQN_'
+        if self.Soft_Update: softupdate = '_soft'
+        if self.dueling: dueling = '_Dueling'
+        if self.epsilot_greedy: greedy = '_Greedy'
+        if self.USE_PER: PER = '_PER'
+        try:
+            pylab.savefig(dqn+self.env_name+softupdate+dueling+greedy+PER+".png")
+        except OSError:
+            pass
+
+        return str(self.average[-1])[:5]
+    
+    def run(self):
+        decay_step = 0
+        for e in range(self.EPISODES):
+            state = self.env.reset()
+            state = np.reshape(state, [1, self.state_size])
+            done = False
+            i = 0
+            while not done:
+                #self.env.render()
+                decay_step += 1
+                action, explore_probability = self.act(state, decay_step)
+                next_state, reward, done, _ = self.env.step(action)
+                next_state = np.reshape(next_state, [1, self.state_size])
+                if not done or i == self.env._max_episode_steps-1:
+                    reward = reward
+                else:
+                    reward = -100
+                self.remember(state, action, reward, next_state, done)
+                state = next_state
+                i += 1
+                if done:
+                    # every step update target model
+                    self.update_target_model()
+                    
+                    # every episode, plot the result
+                    average = self.PlotModel(i, e)
+                     
+                    print("episode: {}/{}, score: {}, e: {:.2}, average: {}".format(e, self.EPISODES, i, explore_probability, average))
+                    if i == self.env._max_episode_steps:
+                        print("Saving trained model to", self.Model_name)
+                        #self.save(self.Model_name)
+                        break
+                self.replay()
+        self.env.close()
+
+    def test(self):
+        self.load(self.Model_name)
+        for e in range(self.EPISODES):
+            state = self.env.reset()
+            state = np.reshape(state, [1, self.state_size])
+            done = False
+            i = 0
+            while not done:
+                self.env.render()
+                action = np.argmax(self.model.predict(state))
+                next_state, reward, done, _ = self.env.step(action)
+                state = np.reshape(next_state, [1, self.state_size])
+                i += 1
+                if done:
+                    print("episode: {}/{}, score: {}".format(e, self.EPISODES, i))
+                    break
+
+if __name__ == "__main__":
+    env_name = 'CartPole-v1'
+    agent = DQNAgent(env_name)
+    agent.run()
+    #agent.test()
