Fix none in simplereflex

aima/agents.py

@@ -191,7 +191,8 @@ def rule_match(state, rules):
 # ______________________________________________________________________________
 
 
-loc_A, loc_B = (0, 0), (1, 0)  # The two locations for the Vacuum world
+loc_A, loc_B, loc_C, loc_D = (0, 0), (1, 0), (0, 1), (1, 1)  # The four locations for the Vacuum world
+locations = [loc_A, loc_B, loc_C, loc_D]
 
 
 def RandomVacuumAgent():
@@ -203,7 +204,7 @@ def RandomVacuumAgent():
     >>> environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
     True
     """
-    return Agent(RandomAgentProgram(['Right', 'Left', 'Suck', 'NoOp']))
+    return Agent(RandomAgentProgram(['Right', 'Left','Up','Down', 'Suck', 'NoOp']))
 
 
 def TableDrivenVacuumAgent():
@@ -261,21 +262,26 @@ def ModelBasedVacuumAgent():
     >>> environment.status == {(1,0):'Clean' , (0,0) : 'Clean'}
     True
     """
-    model = {loc_A: None, loc_B: None}
+    model = {loc_A: None, loc_B: None, loc_C: None, loc_D: None}
 
     def program(percept):
         """Same as ReflexVacuumAgent, except if everything is clean, do NoOp."""
         location, status = percept
-        model[location] = status  # Update the model here
-        if model[loc_A] == model[loc_B] == 'Clean':
+        model[location] = status
+
+        if all(model[loc] == 'Clean' for loc in locations):
             return 'NoOp'
         elif status == 'Dirty':
             return 'Suck'
+        # map: A right to C up to D left to B down
         elif location == loc_A:
-            return 'Right'
+            return 'Up'  # A -> C
+        elif location == loc_C:
+            return 'Right'  # C -> D
+        elif location == loc_D:
+            return 'Down'  # D -> B
         elif location == loc_B:
-            return 'Left'
-
+            return 'Left'  # B -> A
     return Agent(program)
 
 
@@ -806,8 +812,7 @@ class TrivialVacuumEnvironment(Environment):
 
     def __init__(self):
         super().__init__()
-        self.status = {loc_A: random.choice(['Clean', 'Dirty']),
-                       loc_B: random.choice(['Clean', 'Dirty'])}
+        self.status = {loc: random.choice(['Clean', 'Dirty']) for loc in locations}
 
     def thing_classes(self):
         """Return the Thing/Agent classes that may populate this vacuum world."""
@@ -820,11 +825,16 @@ def percept(self, agent):
     def execute_action(self, agent, action):
         """Change agent's location and/or location's status; track performance.
         Score 10 for each dirt cleaned; -1 for each move."""
-        if action == 'Right':
-            agent.location = loc_B
-            agent.performance -= 1
-        elif action == 'Left':
-            agent.location = loc_A
+        a, b = agent.location
+        moves = {'Right': (a + 1, b),
+                 'Left': (a - 1, b),
+                 'Up': (a, b + 1),
+                 'Down': (a, b - 1)
+            }
+        if action in moves:
+            target = moves[action]
+            if target in self.status:
+                agent.location = target
             agent.performance -= 1
         elif action == 'Suck':
             if self.status[agent.location] == 'Dirty':
@@ -833,7 +843,7 @@ def execute_action(self, agent, action):
 
     def default_location(self, thing):
         """Agents start in either location at random."""
-        return random.choice([loc_A, loc_B])
+        return random.choice([loc_A, loc_B, loc_C, loc_D])
 
 
 # ______________________________________________________________________________

aima/notebook_utils.py

@@ -50,7 +50,7 @@ def psource(*functions):
         from pygments.lexers import PythonLexer
         from pygments import highlight
 
-        display(HTML(highlight(source_code, PythonLexer(), HtmlFormatter(full=True))))
+        display(HTML(highlight(source_code, PythonLexer(), HtmlFormatter(noclasses=True, style='monokai'))))
 
     except ImportError:
         print(source_code)

lite/content/Welcome.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "0",
    "metadata": {},
    "source": [
     "# aima-python in the browser\n",
@@ -23,6 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [

lite/content/agents.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2ccc4349",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Agents and environments in the browser\n",
@@ -13,7 +13,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "47375431",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "588ca436",
+   "id": "2",
    "metadata": {},
    "source": [
     "## The two-square vacuum world\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2b51257f",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [

lite/content/csp.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Constraint satisfaction in the browser\n",
@@ -13,6 +14,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,6 +24,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2",
    "metadata": {},
    "source": [
     "## Map colouring\n",
@@ -34,6 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,6 +50,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4",
    "metadata": {},
    "source": [
     "## N-queens\n",
@@ -57,6 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [

lite/content/game_theory.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2a692917",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Game theory in the browser\n",
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27833b2a",
+   "id": "1",
    "metadata": {},
    "source": [
     "`aima.game_theory` uses SciPy for the linear program behind zero-sum games, so we load it alongside `aima`."
@@ -21,7 +21,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ea7846ee",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6470bac",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Pure-strategy Nash equilibria\n",
@@ -43,7 +43,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d78b2ad1",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "148ca176",
+   "id": "5",
    "metadata": {},
    "source": [
     "## Zero-sum games and cooperative value\n",
@@ -70,7 +70,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31cdf1f3",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [

lite/content/games.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Adversarial search in the browser\n",
@@ -13,6 +14,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,6 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,6 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [

lite/content/knowledge.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6433848c",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Knowledge in learning, in the browser\n",
@@ -13,7 +13,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ed8fbc35",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b6b3c18",
+   "id": "2",
    "metadata": {},
    "source": [
     "## Learning a concept from examples\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5a8d03da",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [

lite/content/learning.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "14ed9989",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Learning from examples in the browser\n",
@@ -13,7 +13,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9f1978e2",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba4b1402",
+   "id": "2",
    "metadata": {},
    "source": [
     "## The \"play tennis\" dataset\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9e3ee8d7",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "165a1f98",
+   "id": "4",
    "metadata": {},
    "source": [
     "## Decision tree vs. naive Bayes"
@@ -64,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "14f3bdb8",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [