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update from upstream & make the implement more robust and meaningful in DP/Policy Evaluation Solution #105

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update from upstream & make the implement more robust and meaningful in DP/Policy Evaluation Solution #105

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6ae6e85
update from upstream & make the implement more robust and meaningful
liu-jc Aug 24, 2017
87901eb
fix something wrong
liu-jc Aug 24, 2017
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59 changes: 28 additions & 31 deletions DP/Policy Evaluation Solution.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": 53,
"metadata": {
"collapsed": false
},
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
Expand All @@ -18,7 +16,7 @@
},
{
"cell_type": "code",
"execution_count": 54,
"execution_count": 2,
"metadata": {
"collapsed": true
},
Expand All @@ -30,7 +28,7 @@
},
{
"cell_type": "code",
"execution_count": 55,
"execution_count": 3,
"metadata": {
"collapsed": true
},
Expand All @@ -43,9 +41,9 @@
" Args:\n",
" policy: [S, A] shaped matrix representing the policy.\n",
" env: OpenAI env. env.P represents the transition probabilities of the environment.\n",
" env.P[s][a] is a (prob, next_state, reward, done) tuple.\n",
" env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\n",
" theta: We stop evaluation once our value function change is less than theta for all states.\n",
" discount_factor: lambda discount factor.\n",
" discount_factor: gamma discount factor.\n",
" \n",
" Returns:\n",
" Vector of length env.nS representing the value function.\n",
Expand All @@ -54,18 +52,23 @@
" V = np.zeros(env.nS)\n",
" while True:\n",
" delta = 0\n",
" new_V = np.copy(V) # new_V is V_{k+1} \n",
" # For each state, perform a \"full backup\"\n",
" for s in range(env.nS):\n",
" v = 0\n",
" # Look at the possible next actions\n",
" for a, action_prob in enumerate(policy[s]):\n",
" # For each action, look at the possible next states...\n",
" immediate_reward = env.P[s][a][0][2]\n",
" v_prim = 0\n",
" for prob, next_state, reward, done in env.P[s][a]:\n",
" # Calculate the expected value\n",
" v += action_prob * prob * (reward + discount_factor * V[next_state])\n",
" v_prim += discount_factor * prob * V[next_state]\n",
" v += action_prob * (immediate_reward + v_prim)\n",
" new_V[s] = v\n",
" # How much our value function changed (across any states)\n",
" delta = max(delta, np.abs(v - V[s]))\n",
" V[s] = v\n",
" V = new_V \n",
" # Stop evaluating once our value function change is below a threshold\n",
" if delta < theta:\n",
" break\n",
Expand All @@ -74,10 +77,8 @@
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"random_policy = np.ones([env.nS, env.nA]) / env.nA\n",
Expand All @@ -86,25 +87,23 @@
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Value Function:\n",
"[ 0. -13.99993529 -19.99990698 -21.99989761 -13.99993529\n",
" -17.9999206 -19.99991379 -19.99991477 -19.99990698 -19.99991379\n",
" -17.99992725 -13.99994569 -21.99989761 -19.99991477 -13.99994569 0. ]\n",
"[ 0. -13.99989315 -19.99984167 -21.99982282 -13.99989315\n",
" -17.99986052 -19.99984273 -19.99984167 -19.99984167 -19.99984273\n",
" -17.99986052 -13.99989315 -21.99982282 -19.99984167 -13.99989315 0. ]\n",
"\n",
"Reshaped Grid Value Function:\n",
"[[ 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. ]]\n",
"[[ 0. -13.99989315 -19.99984167 -21.99982282]\n",
" [-13.99989315 -17.99986052 -19.99984273 -19.99984167]\n",
" [-19.99984167 -19.99984273 -17.99986052 -13.99989315]\n",
" [-21.99982282 -19.99984167 -13.99989315 0. ]]\n",
"\n"
]
}
Expand All @@ -121,10 +120,8 @@
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Test: Make sure the evaluated policy is what we expected\n",
Expand Down Expand Up @@ -158,9 +155,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}