CODEWITHSUNDEEP
pythontensorflowopenai-gym
rrz0
rrz0

Reputation: 2302

Reinforcement Learning - VPG: invalid index to scalar variable Index Error

I am trying to run vanilla policy gradient algorithm and render the Open AI environment "CartPole-v1".

The code for the algorithm is given below, and runs well without any errors. The Jupyer notebook for this code may be found here.

en%pylab inline
import tensorflow as tf
import tensorflow.keras.backend as K
import numpy as np
import gym
from tqdm import trange
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.layers import *

env = gym.make("CartPole-v1")
env.observation_space, env.action_space

x = in1 = Input(env.observation_space.shape)
x = Dense(32)(x)
x = Activation('tanh')(x)
x = Dense(env.action_space.n)(x)
x = Lambda(lambda x: tf.nn.log_softmax(x, axis=-1))(x)
m = Model(in1, x)
def loss(y_true, y_pred):
  # y_pred is the log probs of the actions
  # y_true is the action mask weighted by sum of rewards
  return -tf.reduce_sum(y_true*y_pred, axis=-1)
m.compile(Adam(1e-2), loss)
m.summary()
lll = []

# this is like 5x faster than calling m.predict and picking in numpy
pf = K.function(m.layers[0].input, tf.random.categorical(m.layers[-1].output, 1)[0])

tt = trange(40)
for epoch in tt:
  X,Y = [], []
  ll = []
  while len(X) < 8192:
    obs = env.reset()
    acts, rews = [], []
    while True:
      # pick action
      #act_dist = np.exp(m.predict_on_batch(obs[None])[0])
      #act = np.random.choice(range(env.action_space.n), p=act_dist)

      # pick action (fast!)
      act = pf(obs[None])[0]

      # save this state action pair
      X.append(np.copy(obs))
      acts.append(act)

      # take the action
      obs, rew, done, _ = env.step(act)
      rews.append(rew)

      if done:
        for i, act in enumerate(acts):
          act_mask = np.zeros((env.action_space.n))
          act_mask[act] = np.sum(rews[i:])
          Y.append(act_mask)
        ll.append(np.sum(rews))
        break

  loss = m.train_on_batch(np.array(X), np.array(Y))
  lll.append((np.mean(ll), loss))
  tt.set_description("ep_rew:%7.2f    loss:%7.2f" % lll[-1])
  tt.refresh()

plot([x[0] for x in lll], label="Mean Episode Reward")
plot([x[1] for x in lll], label="Epoch Loss")
plt.legend()

enter image description here

When I try to render the environment I get an IndexError:

obs = env.reset()
rews = []
while True:
  env.render()
  pred, act = [x[0] for x in pf(obs[None])]
  obs, rew, done, _ = env.step(np.argmax(pred))
  rews.append(rew)
  time.sleep(0.05)
  if done:
    break
print("ran %d steps, got %f reward" % (len(rews), np.sum(rews)))

in (.0) 3 while True: 4 env.render() ----> 5 pred, act = [x[0] for x in pf(obs[None])] 6 obs, rew, done, _ = env.step(np.argmax(pred)) 7 rews.append(rew)

IndexError: invalid index to scalar variable.

I read that this happens when you try to index a numpy scalar such as numpy.int64 or numpy.float64, however I am not sure where the error is stemming from and how I should go about solving this issue. Any help or suggestions would be appreciated.

Upvotes: 0

Views: 681

Answers (1)

ziedaniel1
ziedaniel1

Reputation: 360

Looks like you might've changed the way pf works but forgotten to update your rendering code.

Try this (I haven't tested it):

  act, = pf(obs[None])  # same as pf(obs[None])[0] but asserts shape
  obs, rew, done, _ = env.step(act)

This will select the action randomly like at training time -- if you want the greedy action you'll need to change a few more things.

Upvotes: 2

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