update

Author: bob7783

rl3/a2c/a2c.py

@@ -0,0 +1,214 @@
+# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence
+import time
+import joblib
+import numpy as np
+import tensorflow as tf
+import os
+
+
+def set_global_seeds(i):
+    tf.set_random_seed(i)
+    np.random.seed(i)
+
+
+def cat_entropy(logits):
+    a0 = logits - tf.reduce_max(logits, 1, keepdims=True)
+    ea0 = tf.exp(a0)
+    z0 = tf.reduce_sum(ea0, 1, keepdims=True)
+    p0 = ea0 / z0
+    return tf.reduce_sum(p0 * (tf.log(z0) - a0), 1)
+
+
+def find_trainable_variables(key):
+    with tf.variable_scope(key):
+        return tf.trainable_variables()
+
+
+def discount_with_dones(rewards, dones, gamma):
+    discounted = []
+    r = 0
+    for reward, done in zip(rewards[::-1], dones[::-1]):
+        r = reward + gamma * r * (1. - done)  # fixed off by one bug
+        discounted.append(r)
+    return discounted[::-1]
+
+
+
+class Agent:
+    def __init__(self, Network, ob_space, ac_space, nenvs, nsteps, nstack,
+                 ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4,
+                 alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6)):
+        config = tf.ConfigProto(intra_op_parallelism_threads=nenvs,
+                                inter_op_parallelism_threads=nenvs)
+        config.gpu_options.allow_growth = True
+        sess = tf.Session(config=config)
+        nbatch = nenvs * nsteps
+
+        A = tf.placeholder(tf.int32, [nbatch])
+        ADV = tf.placeholder(tf.float32, [nbatch])
+        R = tf.placeholder(tf.float32, [nbatch])
+        LR = tf.placeholder(tf.float32, [])
+
+        step_model = Network(sess, ob_space, ac_space, nenvs, 1, nstack, reuse=False)
+        train_model = Network(sess, ob_space, ac_space, nenvs, nsteps, nstack, reuse=True)
+
+        neglogpac = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=train_model.pi, labels=A)
+        pg_loss = tf.reduce_mean(ADV * neglogpac)
+        vf_loss = tf.reduce_mean(tf.squared_difference(tf.squeeze(train_model.vf), R) / 2.0)
+        entropy = tf.reduce_mean(cat_entropy(train_model.pi))
+        loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef
+
+        params = find_trainable_variables("model")
+        grads = tf.gradients(loss, params)
+        if max_grad_norm is not None:
+            grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
+        grads_and_params = list(zip(grads, params))
+        trainer = tf.train.RMSPropOptimizer(learning_rate=LR, decay=alpha, epsilon=epsilon)
+        _train = trainer.apply_gradients(grads_and_params)
+
+        def train(states, rewards, actions, values):
+            advs = rewards - values
+            feed_dict = {train_model.X: states, A: actions, ADV: advs, R: rewards, LR: lr}
+            policy_loss, value_loss, policy_entropy, _ = sess.run(
+                [pg_loss, vf_loss, entropy, _train],
+                feed_dict
+            )
+            return policy_loss, value_loss, policy_entropy
+
+        def save(save_path):
+            ps = sess.run(params)
+            joblib.dump(ps, save_path)
+
+        def load(load_path):
+            loaded_params = joblib.load(load_path)
+            restores = []
+            for p, loaded_p in zip(params, loaded_params):
+                restores.append(p.assign(loaded_p))
+            ps = sess.run(restores)
+
+        self.train = train
+        self.train_model = train_model
+        self.step_model = step_model
+        self.step = step_model.step
+        self.value = step_model.value
+        self.save = save
+        self.load = load
+        tf.global_variables_initializer().run(session=sess)
+
+
+class Runner:
+    def __init__(self, env, agent, nsteps=5, nstack=4, gamma=0.99):
+        self.env = env
+        self.agent = agent
+        nh, nw, nc = env.observation_space.shape
+        nenv = env.num_envs
+        self.batch_ob_shape = (nenv * nsteps, nh, nw, nc * nstack)
+        self.state = np.zeros((nenv, nh, nw, nc * nstack), dtype=np.uint8)
+        self.nc = nc
+        obs = env.reset()
+        self.update_state(obs)
+        self.gamma = gamma
+        self.nsteps = nsteps
+        self.dones = [False for _ in range(nenv)]
+        self.total_rewards = [] # store all workers' total rewards
+        self.real_total_rewards = []
+
+    def update_state(self, obs):
+        # Do frame-stacking here instead of the FrameStack wrapper to reduce IPC overhead
+        self.state = np.roll(self.state, shift=-self.nc, axis=3)
+        self.state[:, :, :, -self.nc:] = obs
+
+    def run(self):
+        mb_states, mb_rewards, mb_actions, mb_values, mb_dones = [], [], [], [], []
+        for n in range(self.nsteps):
+            actions, values = self.agent.step(self.state)
+            mb_states.append(np.copy(self.state))
+            mb_actions.append(actions)
+            mb_values.append(values)
+            mb_dones.append(self.dones)
+            obs, rewards, dones, infos = self.env.step(actions)
+            for done, info in zip(dones, infos):
+                if done:
+                    self.total_rewards.append(info['reward'])
+                    if info['total_reward'] != -1:
+                        self.real_total_rewards.append(info['total_reward'])
+            self.dones = dones
+            for n, done in enumerate(dones):
+                if done:
+                    self.state[n] = self.state[n] * 0
+            self.update_state(obs)
+            mb_rewards.append(rewards)
+        mb_dones.append(self.dones)
+        # batch of steps to batch of rollouts
+        mb_states = np.asarray(mb_states, dtype=np.uint8).swapaxes(1, 0).reshape(self.batch_ob_shape)
+        mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0)
+        mb_actions = np.asarray(mb_actions, dtype=np.int32).swapaxes(1, 0)
+        mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0)
+        mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
+        mb_dones = mb_dones[:, 1:]
+        last_values = self.agent.value(self.state).tolist()
+        # discount/bootstrap off value fn
+        for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)):
+            rewards = rewards.tolist()
+            dones = dones.tolist()
+            if dones[-1] == 0:
+                rewards = discount_with_dones(rewards + [value], dones + [0], self.gamma)[:-1]
+            else:
+                rewards = discount_with_dones(rewards, dones, self.gamma)
+            mb_rewards[n] = rewards
+        mb_rewards = mb_rewards.flatten()
+        mb_actions = mb_actions.flatten()
+        mb_values = mb_values.flatten()
+        return mb_states, mb_rewards, mb_actions, mb_values
+
+
+def learn(network, env, seed, new_session=True,  nsteps=5, nstack=4, total_timesteps=int(80e6),
+          vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4,
+          epsilon=1e-5, alpha=0.99, gamma=0.99, log_interval=1000):
+    tf.reset_default_graph()
+    set_global_seeds(seed)
+
+    nenvs = env.num_envs
+    env_id = env.env_id
+    save_name = os.path.join('models', env_id + '.save')
+    ob_space = env.observation_space
+    ac_space = env.action_space
+    agent = Agent(Network=network, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs,
+                  nsteps=nsteps, nstack=nstack,
+                  ent_coef=ent_coef, vf_coef=vf_coef,
+                  max_grad_norm=max_grad_norm,
+                  lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps)
+    if os.path.exists(save_name):
+        agent.load(save_name)
+
+    runner = Runner(env, agent, nsteps=nsteps, nstack=nstack, gamma=gamma)
+
+    nbatch = nenvs * nsteps
+    tstart = time.time()
+    for update in range(1, total_timesteps // nbatch + 1):
+        states, rewards, actions, values = runner.run()
+        policy_loss, value_loss, policy_entropy = agent.train(
+            states, rewards, actions, values)
+        nseconds = time.time() - tstart
+        fps = int((update * nbatch) / nseconds)
+        if update % log_interval == 0 or update == 1:
+            print(' - - - - - - - ')
+            print("nupdates", update)
+            print("total_timesteps", update * nbatch)
+            print("fps", fps)
+            print("policy_entropy", float(policy_entropy))
+            print("value_loss", float(value_loss))
+
+            # total reward
+            r = runner.total_rewards[-100:] # get last 100
+            tr = runner.real_total_rewards[-100:]
+            if len(r) == 100:
+                print("avg reward (last 100):", np.mean(r))
+            if len(tr) == 100:
+                print("avg total reward (last 100):", np.mean(tr))
+                print("max (last 100):", np.max(tr))
+
+            agent.save(save_name)
+
+    env.close()
+    agent.save(save_name)