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sappelt/frozendeepq.ipynb

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FrozenLake with q-tables and refactored to deep q-learning
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frozendeepq.ipynb
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frozenq.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import gym\n",
"\n",
"import random"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"env = gym.make(\"FrozenLake-v0\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"16\n",
"\n",
"\u001b[41mS\u001b[0mFFF\n",
"FHFH\n",
"FFFH\n",
"HFFG\n"
]
}
],
"source": [
"action_size = env.action_space.n\n",
"state_size = env.observation_space.n\n",
"\n",
"# Actions are left, up, right, down\n",
"print(action_size)\n",
"# States are the 16 fields\n",
"print(state_size)\n",
"env.render()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]\n",
" [0. 0. 0. 0.]]\n"
]
}
],
"source": [
"# q table where rows=states, columns=actions\n",
"qtable = np.zeros((state_size, action_size))\n",
"print(qtable)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"total_episodes = 15000 # Total episodes\n",
"learning_rate = 0.8 # Learning rate\n",
"max_steps = 99 # Max steps per episode\n",
"gamma = 0.95 # Discounting rate\n",
"\n",
"# Exploration parameters\n",
"epsilon = 1.0 # Exploration rate\n",
"max_epsilon = 1.0 # Exploration probability at start\n",
"min_epsilon = 0.01 # Minimum exploration probability \n",
"decay_rate = 0.005 # Exponential decay rate for exploration prob"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"text": [
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