ppo in tensorflow 2

ReinforcementLearning/PolicyGradient/PPO/tf2/agent.py

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+import numpy as np
+import tensorflow as tf
+import tensorflow.keras as keras
+from tensorflow.keras.optimizers import Adam
+import tensorflow_probability as tfp
+from memory import PPOMemory
+from networks import ActorNetwork, CriticNetwork
+
+
+class Agent:
+    def __init__(self, n_actions, input_dims, gamma=0.99, alpha=0.0003,
+                 gae_lambda=0.95, policy_clip=0.2, batch_size=64,
+                 n_epochs=10, chkpt_dir='models/'):
+        self.gamma = gamma
+        self.policy_clip = policy_clip
+        self.n_epochs = n_epochs
+        self.gae_lambda = gae_lambda
+        self.chkpt_dir = chkpt_dir
+
+        self.actor = ActorNetwork(n_actions)
+        self.actor.compile(optimizer=Adam(learning_rate=alpha))
+        self.critic = CriticNetwork()
+        self.critic.compile(optimizer=Adam(learning_rate=alpha))
+        self.memory = PPOMemory(batch_size)
+
+    def store_transition(self, state, action, probs, vals, reward, done):
+        self.memory.store_memory(state, action, probs, vals, reward, done)
+
+    def save_models(self):
+        print('... saving models ...')
+        self.actor.save(self.chkpt_dir + 'actor')
+        self.critic.save(self.chkpt_dir + 'critic')
+
+    def load_models(self):
+        print('... loading models ...')
+        self.actor = keras.models.load_model(self.chkpt_dir + 'actor')
+        self.critic = keras.models.load_model(self.chkpt_dir + 'critic')
+
+    def choose_action(self, observation):
+        state = tf.convert_to_tensor([observation])
+
+        probs = self.actor(state)
+        dist = tfp.distributions.Categorical(probs)
+        action = dist.sample()
+        log_prob = dist.log_prob(action)
+        value = self.critic(state)
+
+        action = action.numpy()[0]
+        value = value.numpy()[0]
+        log_prob = log_prob.numpy()[0]
+
+        return action, log_prob, value
+
+    def learn(self):
+        for _ in range(self.n_epochs):
+            state_arr, action_arr, old_prob_arr, vals_arr,\
+                reward_arr, dones_arr, batches = \
+                self.memory.generate_batches()
+
+            values = vals_arr
+            advantage = np.zeros(len(reward_arr), dtype=np.float32)
+
+            for t in range(len(reward_arr)-1):
+                discount = 1
+                a_t = 0
+                for k in range(t, len(reward_arr)-1):
+                    a_t += discount*(reward_arr[k] + self.gamma*values[k+1] * (
+                        1-int(dones_arr[k])) - values[k])
+                    discount *= self.gamma*self.gae_lambda
+                advantage[t] = a_t
+
+            for batch in batches:
+                with tf.GradientTape(persistent=True) as tape:
+                    states = tf.convert_to_tensor(state_arr[batch])
+                    old_probs = tf.convert_to_tensor(old_prob_arr[batch])
+                    actions = tf.convert_to_tensor(action_arr[batch])
+
+                    probs = self.actor(states)
+                    dist = tfp.distributions.Categorical(probs)
+                    new_probs = dist.log_prob(actions)
+
+                    critic_value = self.critic(states)
+
+                    critic_value = tf.squeeze(critic_value, 1)
+
+                    prob_ratio = tf.math.exp(new_probs - old_probs)
+                    weighted_probs = advantage[batch] * prob_ratio
+                    clipped_probs = tf.clip_by_value(prob_ratio,
+                                                     1-self.policy_clip,
+                                                     1+self.policy_clip)
+                    weighted_clipped_probs = clipped_probs * advantage[batch]
+                    actor_loss = -tf.math.minimum(weighted_probs,
+                                                  weighted_clipped_probs)
+                    actor_loss = tf.math.reduce_mean(actor_loss)
+
+                    returns = advantage[batch] + values[batch]
+                    # critic_loss = tf.math.reduce_mean(tf.math.pow(
+                    #                                  returns-critic_value, 2))
+                    critic_loss = keras.losses.MSE(critic_value, returns)
+
+                actor_params = self.actor.trainable_variables
+                actor_grads = tape.gradient(actor_loss, actor_params)
+                critic_params = self.critic.trainable_variables
+                critic_grads = tape.gradient(critic_loss, critic_params)
+                self.actor.optimizer.apply_gradients(
+                        zip(actor_grads, actor_params))
+                self.critic.optimizer.apply_gradients(
+                        zip(critic_grads, critic_params))
+
+        self.memory.clear_memory()

ReinforcementLearning/PolicyGradient/PPO/tf2/main.py

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+import gym
+import numpy as np
+from agent import Agent
+from utils import plot_learning_curve
+
+if __name__ == '__main__':
+    env = gym.make('CartPole-v0')
+    N = 20
+    batch_size = 5
+    n_epochs = 4
+    alpha = 0.0003
+    agent = Agent(n_actions=env.action_space.n, batch_size=batch_size,
+                  alpha=alpha, n_epochs=n_epochs,
+                  input_dims=env.observation_space.shape)
+    n_games = 300
+
+    figure_file = 'plots/cartpole.png'
+
+    best_score = env.reward_range[0]
+    score_history = []
+
+    learn_iters = 0
+    avg_score = 0
+    n_steps = 0
+
+    for i in range(n_games):
+        observation = env.reset()
+        done = False
+        score = 0
+        while not done:
+            action, prob, val = agent.choose_action(observation)
+            observation_, reward, done, info = env.step(action)
+            n_steps += 1
+            score += reward
+            agent.store_transition(observation, action,
+                                   prob, val, reward, done)
+            if n_steps % N == 0:
+                agent.learn()
+                learn_iters += 1
+            observation = observation_
+        score_history.append(score)
+        avg_score = np.mean(score_history[-100:])
+
+        if avg_score > best_score:
+            best_score = avg_score
+            agent.save_models()
+
+        print('episode', i, 'score %.1f' % score, 'avg score %.1f' % avg_score,
+              'time_steps', n_steps, 'learning_steps', learn_iters)
+    x = [i+1 for i in range(len(score_history))]
+    plot_learning_curve(x, score_history, figure_file)

ReinforcementLearning/PolicyGradient/PPO/tf2/memory.py

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+import numpy as np
+
+
+class PPOMemory:
+    def __init__(self, batch_size):
+        self.states = []
+        self.probs = []
+        self.vals = []
+        self.actions = []
+        self.rewards = []
+        self.dones = []
+
+        self.batch_size = batch_size
+
+    def generate_batches(self):
+        n_states = len(self.states)
+        batch_start = np.arange(0, n_states, self.batch_size)
+        indices = np.arange(n_states, dtype=np.int64)
+        np.random.shuffle(indices)
+        batches = [indices[i:i+self.batch_size] for i in batch_start]
+
+        return np.array(self.states),\
+            np.array(self.actions),\
+            np.array(self.probs),\
+            np.array(self.vals),\
+            np.array(self.rewards),\
+            np.array(self.dones),\
+            batches
+
+    def store_memory(self, state, action, probs, vals, reward, done):
+        self.states.append(state)
+        self.actions.append(action)
+        self.probs.append(probs)
+        self.vals.append(vals)
+        self.rewards.append(reward)
+        self.dones.append(done)
+
+    def clear_memory(self):
+        self.states = []
+        self.probs = []
+        self.actions = []
+        self.rewards = []
+        self.dones = []
+        self.vals = []

ReinforcementLearning/PolicyGradient/PPO/tf2/networks.py

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+import tensorflow.keras as keras
+from tensorflow.keras.layers import Dense
+
+
+class ActorNetwork(keras.Model):
+    def __init__(self, n_actions, fc1_dims=256, fc2_dims=256):
+        super(ActorNetwork, self).__init__()
+
+        self.fc1 = Dense(fc1_dims, activation='relu')
+        self.fc2 = Dense(fc2_dims, activation='relu')
+        self.fc3 = Dense(n_actions, activation='softmax')
+
+    def call(self, state):
+        x = self.fc1(state)
+        x = self.fc2(x)
+        x = self.fc3(x)
+
+        return x
+
+
+class CriticNetwork(keras.Model):
+    def __init__(self, fc1_dims=256, fc2_dims=256):
+        super(CriticNetwork, self).__init__()
+        self.fc1 = Dense(fc1_dims, activation='relu')
+        self.fc2 = Dense(fc2_dims, activation='relu')
+        self.q = Dense(1, activation=None)
+
+    def call(self, state):
+        x = self.fc1(state)
+        x = self.fc2(x)
+        q = self.q(x)
+
+        return q

ReinforcementLearning/PolicyGradient/PPO/tf2/utils.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+def plot_learning_curve(x, scores, figure_file):
+    running_avg = np.zeros(len(scores))
+    for i in range(len(running_avg)):
+        running_avg[i] = np.mean(scores[max(0, i-100):(i+1)])
+    plt.plot(x, running_avg)
+    plt.title('Running average of previous 100 scores')
+    plt.savefig(figure_file)