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import numpy as np
import pprint
import sys
if "../" not in sys.path:
sys.path.append("../")
from lib.envs.gridworld import GridworldEnv

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``````

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pp = pprint.PrettyPrinter(indent=2)
env = GridworldEnv()

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# Taken from Policy Evaluation Exercise!

def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):
"""
Evaluate a policy given an environment and a full description of the environment's dynamics.

Args:
policy: [S, A] shaped matrix representing the policy.
env: OpenAI env. env.P represents the transition probabilities of the environment.
env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).
env.nS is a number of states in the environment.
env.nA is a number of actions in the environment.
theta: We stop evaluation once our value function change is less than theta for all states.
discount_factor: Gamma discount factor.

Returns:
Vector of length env.nS representing the value function.
"""
V = np.zeros(env.nS)
while True:
delta = 0
# For each state, perform a "full backup"
for s in range(env.nS):
v = 0
# Look at the possible next actions
for a, action_prob in enumerate(policy[s]):
# For each action, look at the possible next states...
for  prob, next_state, reward, done in env.P[s][a]:
# Calculate the expected value
v += action_prob * prob * (reward + discount_factor * V[next_state])
# How much our value function changed (across any states)
delta = max(delta, np.abs(v - V[s]))
V[s] = v
# Stop evaluating once our value function change is below a threshold
if delta < theta:
break
return np.array(V)

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def policy_improvement(env, policy_eval_fn=policy_eval, discount_factor=1.0):
"""
Policy Improvement Algorithm. Iteratively evaluates and improves a policy
until an optimal policy is found.

Args:
env: The OpenAI envrionment.
policy_eval_fn: Policy Evaluation function that takes 3 arguments:
policy, env, discount_factor.
discount_factor: gamma discount factor.

Returns:
A tuple (policy, V).
policy is the optimal policy, a matrix of shape [S, A] where each state s
contains a valid probability distribution over actions.
V is the value function for the optimal policy.

"""
policy = np.ones([env.nS, env.nA]) / env.nA

while True:
# Implement this!
break

return policy, np.zeros(env.nS)

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In :

policy, v = policy_improvement(env)
print("Policy Probability Distribution:")
print(policy)
print("")

print("Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):")
print(np.reshape(np.argmax(policy, axis=1), env.shape))
print("")

print("Value Function:")
print(v)
print("")

print("Reshaped Grid Value Function:")
print(v.reshape(env.shape))
print("")

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Policy Probability Distribution:
[[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]
[ 0.25  0.25  0.25  0.25]]

Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):
[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]

Value Function:
[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]

Reshaped Grid Value Function:
[[ 0.  0.  0.  0.]
[ 0.  0.  0.  0.]
[ 0.  0.  0.  0.]
[ 0.  0.  0.  0.]]

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# Test the value function
expected_v = np.array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1,  0])
np.testing.assert_array_almost_equal(v, expected_v, decimal=2)

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---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-15-55581f8eb5c9> in <module>()
1 # Test the value function
2 expected_v = np.array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1,  0])
----> 3 np.testing.assert_array_almost_equal(v, expected_v, decimal=2)

/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py in assert_array_almost_equal(x, y, decimal, err_msg, verbose)
914     assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
915              header=('Arrays are not almost equal to %d decimals' % decimal),
--> 916              precision=decimal)
917
918

/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py in assert_array_compare(comparison, x, y, err_msg, verbose, header, precision)
735                                 names=('x', 'y'), precision=precision)
736             if not cond:
--> 737                 raise AssertionError(msg)
738     except ValueError:
739         import traceback

AssertionError:
Arrays are not almost equal to 2 decimals

(mismatch 87.5%)
x: array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
0.,  0.,  0.])
y: array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1,  0])

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