class TrivialVacuumEnvironment(Environment):\n",
+ "\n",
+ " """This environment has two locations, A and B. Each can be Dirty\n",
+ " or Clean. The agent perceives its location and the location's\n",
+ " status. This serves as an example of how to implement a simple\n",
+ " Environment."""\n",
+ "\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.status = {loc_A: random.choice(['Clean', 'Dirty']),\n",
+ " loc_B: random.choice(['Clean', 'Dirty'])}\n",
+ "\n",
+ " def thing_classes(self):\n",
+ " return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent,\n",
+ " TableDrivenVacuumAgent, ModelBasedVacuumAgent]\n",
+ "\n",
+ " def percept(self, agent):\n",
+ " """Returns the agent's location, and the location status (Dirty/Clean)."""\n",
+ " return (agent.location, self.status[agent.location])\n",
+ "\n",
+ " def execute_action(self, agent, action):\n",
+ " """Change agent's location and/or location's status; track performance.\n",
+ " Score 10 for each dirt cleaned; -1 for each move."""\n",
+ " if action == 'Right':\n",
+ " agent.location = loc_B\n",
+ " agent.performance -= 1\n",
+ " elif action == 'Left':\n",
+ " agent.location = loc_A\n",
+ " agent.performance -= 1\n",
+ " elif action == 'Suck':\n",
+ " if self.status[agent.location] == 'Dirty':\n",
+ " agent.performance += 10\n",
+ " self.status[agent.location] = 'Clean'\n",
+ "\n",
+ " def default_location(self, thing):\n",
+ " """Agents start in either location at random."""\n",
+ " return random.choice([loc_A, loc_B])\n",
+ "![](\"/files/images/simple_reflex_agent.jpg\")
"
+ "\"\""
]
},
{
@@ -324,7 +462,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
@@ -341,25 +479,29 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
- "# TODO: Implement these functions for two-dimensional environment\n",
- "# Interpret-input function for the two-state environment\n",
- "def interpret_input(percept):\n",
- " pass\n",
"\n",
- "rules = None\n",
+ "loc_A = (0, 0)\n",
+ "loc_B = (1, 0)\n",
+ "\n",
+ "\"\"\"We change the simpleReflexAgentProgram so that it doesn't make use of the Rule class\"\"\"\n",
+ "def SimpleReflexAgentProgram():\n",
+ " \"\"\"This agent takes action based solely on the percept. [Figure 2.10]\"\"\"\n",
+ " \n",
+ " def program(percept):\n",
+ " loc, status = percept\n",
+ " return ('Suck' if status == 'Dirty' \n",
+ " else'Right' if loc == loc_A \n",
+ " else'Left')\n",
+ " return program\n",
"\n",
- "# Rule-match function for the two-state environment\n",
- "def rule_match(state, rule):\n",
- " for rule in rules:\n",
- " if rule.matches(state):\n",
- " return rule \n",
" \n",
"# Create a simple reflex agent the two-state environment\n",
- "simple_reflex_agent = ReflexVacuumAgent()"
+ "program = SimpleReflexAgentProgram()\n",
+ "simple_reflex_agent = Agent(program)"
]
},
{
@@ -371,7 +513,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 51,
"metadata": {},
"outputs": [
{
@@ -390,7 +532,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 52,
"metadata": {},
"outputs": [
{
@@ -398,7 +540,7 @@
"output_type": "stream",
"text": [
"State of the Environment: {(0, 0): 'Clean', (1, 0): 'Clean'}.\n",
- "SimpleReflexVacuumAgent is located at (0, 0).\n"
+ "SimpleReflexVacuumAgent is located at (1, 0).\n"
]
}
],
@@ -551,7 +693,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.3"
+ "version": "3.6.4"
}
},
"nbformat": 4,