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Exercise notebooks with no outputs. #207

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Exercise notebooks with no outputs. #207

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66b331c
Clear output.
Aug 3, 2019
c996882
Solution of the policy evaluation exercise.
Aug 7, 2019
1317b01
Solution of the policy iteration exercise.
Aug 7, 2019
165a89d
Solution of the value iteration exercise.
Aug 7, 2019
d7c1301
Solution of the gamblers problem exercise.
Aug 7, 2019
1bfd12f
Generate episode according to given policy.
Aug 7, 2019
75a018d
Solving the MC black jack prediction problem.
Aug 8, 2019
f85e46f
On policy first visit MC control for blackjack.
Aug 8, 2019
2c49692
Solve backjack by off policy control with weighted important sampling.
Aug 9, 2019
f536846
Correct return computation.
Aug 9, 2019
9ddcd6f
Windy GridWorld Sarsa.
Aug 31, 2019
057c565
Q-Learning for cliff environment.
Aug 31, 2019
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181 changes: 151 additions & 30 deletions DP/Gamblers Problem.ipynb
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83 changes: 47 additions & 36 deletions DP/Policy Evaluation.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,8 @@
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
Expand All @@ -17,21 +15,17 @@
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"env = GridworldEnv()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\n",
Expand All @@ -53,17 +47,27 @@
" # Start with a random (all 0) value function\n",
" V = np.zeros(env.nS)\n",
" while True:\n",
" # TODO: Implement!\n",
" break\n",
" delta = .0\n",
" for s in range(env.nS):\n",
" v = V[s]\n",
" v_new = 0\n",
" \n",
" for a, p_as in enumerate(policy[s]):\n",
" for (p_srsa, next_state, reward, done) in env.P[s][a]:\n",
" v_new += p_as * p_srsa * (reward + discount_factor * V[next_state])\n",
" V[s] = v_new\n",
" \n",
" delta = max(delta, np.abs(v - V[s]))\n",
"\n",
" if delta < theta:\n",
" break\n",
" return np.array(V)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"random_policy = np.ones([env.nS, env.nA]) / env.nA\n",
Expand All @@ -72,35 +76,42 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Test: Make sure the evaluated policy is what we expected\n",
"expected_v = np.array([0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22, -20, -14, 0])\n",
"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 2 decimals\n\n(mismatch 87.5%)\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0.])\n y: array([ 0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22,\n -20, -14, 0])",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-235f39fb115c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Test: Make sure the evaluated policy is what we expected\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mexpected_v\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m22\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m18\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m22\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_array_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_v\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_array_almost_equal\u001b[0;34m(x, y, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 914\u001b[0m assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,\n\u001b[1;32m 915\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Arrays are not almost equal to %d decimals'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m precision=decimal)\n\u001b[0m\u001b[1;32m 917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision)\u001b[0m\n\u001b[1;32m 735\u001b[0m names=('x', 'y'), precision=precision)\n\u001b[1;32m 736\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcond\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 738\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 2 decimals\n\n(mismatch 87.5%)\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0.])\n y: array([ 0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22,\n -20, -14, 0])"
]
"data": {
"text/plain": [
"array([ 0. , -13.99993529, -19.99990698, -21.99989761,\n",
" -13.99993529, -17.9999206 , -19.99991379, -19.99991477,\n",
" -19.99990698, -19.99991379, -17.99992725, -13.99994569,\n",
" -21.99989761, -19.99991477, -13.99994569, 0. ])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Test: Make sure the evaluated policy is what we expected\n",
"expected_v = np.array([0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22, -20, -14, 0])\n",
"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)"
"v"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": []
}
Expand Down
135 changes: 71 additions & 64 deletions DP/Policy Iteration.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,8 @@
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
Expand All @@ -18,10 +16,8 @@
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"pp = pprint.PrettyPrinter(indent=2)\n",
Expand All @@ -30,10 +26,8 @@
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Taken from Policy Evaluation Exercise!\n",
Expand Down Expand Up @@ -78,10 +72,8 @@
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def policy_improvement(env, policy_eval_fn=policy_eval, discount_factor=1.0):\n",
Expand All @@ -102,57 +94,81 @@
" V is the value function for the optimal policy.\n",
" \n",
" \"\"\"\n",
" \n",
" def greedy_action(s, V, env):\n",
" A = np.zeros(env.nA)\n",
" for a in range(env.nA):\n",
" for prob, next_state, reward, done in env.P[s][a]:\n",
" A[a] += prob * (reward + discount_factor * V[next_state])\n",
" \n",
" return A.argmax()\n",
" \n",
" # Start with a random policy\n",
" policy = np.ones([env.nS, env.nA]) / env.nA\n",
" Vstar = None\n",
" \n",
" while True:\n",
" # Implement this!\n",
" break\n",
" V = policy_eval_fn(policy, env, discount_factor)\n",
" \n",
" policy_stable = True\n",
" for s in range(env.nS):\n",
" # given the initial policy is a random policy we should instead\n",
" # sample from the corresponding probability distribution\n",
" action = policy[s].argmax()\n",
" best_action = greedy_action(s, V, env)\n",
" \n",
" policy[s] = np.eye(env.nA)[best_action]\n",
" if action != best_action:\n",
" policy_stable = False\n",
" \n",
" if policy_stable:\n",
" Vstar = V\n",
" break\n",
" \n",
" return policy, np.zeros(env.nS)"
" return policy, Vstar"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Policy Probability Distribution:\n",
"[[ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]\n",
" [ 0.25 0.25 0.25 0.25]]\n",
"[[1. 0. 0. 0.]\n",
" [0. 0. 0. 1.]\n",
" [0. 0. 0. 1.]\n",
" [0. 0. 1. 0.]\n",
" [1. 0. 0. 0.]\n",
" [1. 0. 0. 0.]\n",
" [1. 0. 0. 0.]\n",
" [0. 0. 1. 0.]\n",
" [1. 0. 0. 0.]\n",
" [1. 0. 0. 0.]\n",
" [0. 1. 0. 0.]\n",
" [0. 0. 1. 0.]\n",
" [1. 0. 0. 0.]\n",
" [0. 1. 0. 0.]\n",
" [0. 1. 0. 0.]\n",
" [1. 0. 0. 0.]]\n",
"\n",
"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\n",
"[[0 0 0 0]\n",
" [0 0 0 0]\n",
" [0 0 0 0]\n",
" [0 0 0 0]]\n",
"[[0 3 3 2]\n",
" [0 0 0 2]\n",
" [0 0 1 2]\n",
" [0 1 1 0]]\n",
"\n",
"Value Function:\n",
"[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
"[ 0. -1. -2. -3. -1. -2. -3. -2. -2. -3. -2. -1. -3. -2. -1. 0.]\n",
"\n",
"Reshaped Grid Value Function:\n",
"[[ 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0.]]\n",
"[[ 0. -1. -2. -3.]\n",
" [-1. -2. -3. -2.]\n",
" [-2. -3. -2. -1.]\n",
" [-3. -2. -1. 0.]]\n",
"\n"
]
}
Expand All @@ -179,23 +195,9 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "AssertionError",
"evalue": "\nArrays are not almost equal to 2 decimals\n\n(mismatch 87.5%)\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0.])\n y: array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-55581f8eb5c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Test the value function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mexpected_v\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_array_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_v\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_array_almost_equal\u001b[0;34m(x, y, decimal, err_msg, verbose)\u001b[0m\n\u001b[1;32m 914\u001b[0m assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,\n\u001b[1;32m 915\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Arrays are not almost equal to %d decimals'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m precision=decimal)\n\u001b[0m\u001b[1;32m 917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\u001b[0m in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision)\u001b[0m\n\u001b[1;32m 735\u001b[0m names=('x', 'y'), precision=precision)\n\u001b[1;32m 736\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcond\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 738\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 739\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: \nArrays are not almost equal to 2 decimals\n\n(mismatch 87.5%)\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n 0., 0., 0.])\n y: array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])"
]
}
],
"outputs": [],
"source": [
"# Test the value function\n",
"expected_v = np.array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\n",
Expand All @@ -205,9 +207,14 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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