# %load ../../scripts/cartpole/sarsa.py
import gym
import keras_gym as km
from tensorflow import keras
# the cart-pole MDP
env = gym.make('CartPole-v0')
class Linear(km.FunctionApproximator):
""" linear function approximator """
def body(self, X):
# body is trivial, only flatten and then pass to head (one dense layer)
return keras.layers.Flatten()(X)
# value function and its derived policy
func = Linear(env, lr=0.001)
q = km.QTypeI(func, update_strategy='sarsa')
policy = km.EpsilonGreedy(q)
# static parameters
num_episodes = 200
num_steps = env.spec.max_episode_steps
# used for early stopping
num_consecutive_successes = 0
# train
for ep in range(num_episodes):
s = env.reset()
policy.epsilon = 0.1 if ep < 10 else 0.01
for t in range(num_steps):
a = policy(s)
s_next, r, done, info = env.step(a)
q.update(s, a, r, done)
if done:
if t == num_steps - 1:
num_consecutive_successes += 1
print("num_consecutive_successes: {}"
.format(num_consecutive_successes))
else:
num_consecutive_successes = 0
print("failed after {} steps".format(t))
break
s = s_next
if num_consecutive_successes == 10:
break
# run env one more time to render
km.render_episode(env, policy, step_delay_ms=25)