Single Agent: Battleship#
Introduction#
This notebook will focus on using trace to optimize multiple pieces of code automatically in the context of a Battleship game.
Setup and Installation#
Let’s start by importing the necessary libraries.
%pip install trace-opt
import random
import numpy as np
from opto.trace import bundle, node, Module, GRAPH
from opto.trace.errors import ExecutionError
from opto.trace.bundle import ExceptionNode
from opto.optimizers import OptoPrime
from autogen import config_list_from_json
Show code cell source
import os
import ipywidgets as widgets
from IPython.display import display
# Function to save the environment variable and API key
def save_env_variable(env_name, api_key):
# Validate inputs
if not env_name.strip():
print("⚠️ Environment variable name cannot be empty.")
return
if not api_key.strip():
print("⚠️ API key cannot be empty.")
return
# Store the API key as an environment variable
os.environ[env_name] = api_key
globals()[env_name] = api_key # Set it as a global variable
print(f"✅ API key has been set for environment variable: {env_name}")
# Create the input widgets
env_name_input = widgets.Text(
value="OPENAI_API_KEY", # Default value
description="Env Name:",
placeholder="Enter env variable name (e.g., MY_API_KEY)",
)
api_key_input = widgets.Password(
description="API Key:",
placeholder="Enter your API key",
)
# Create the button to submit the inputs
submit_button = widgets.Button(description="Set API Key")
# Display the widgets
display(env_name_input, api_key_input, submit_button)
# Callback function for the button click
def on_button_click(b):
env_name = env_name_input.value
api_key = api_key_input.value
save_env_variable(env_name, api_key)
# Attach the callback to the button
submit_button.on_click(on_button_click)
Environment Setup#
Define the Battleship board creation and environment handling functions.
def create_battleship_board(width, height):
board = [['.' for _ in range(width)] for _ in range(height)]
return board
def can_place_ship(board, row, col, size, is_vertical):
if is_vertical:
if row + size > len(board):
return False
for i in range(size):
if board[row + i][col] != '.':
return False
else:
if col + size > len(board[0]):
return False
for i in range(size):
if board[row][col + i] != '.':
return False
return True
def place_ship(board, row, col, size, is_vertical, ship_symbol):
if is_vertical:
for i in range(size):
board[row + i][col] = ship_symbol
else:
for i in range(size):
board[row][col + i] = ship_symbol
def create_and_fill_battleship_board(width, height, ships, num_each_type=2):
board = [['.' for _ in range(width)] for _ in range(height)]
for ship_symbol, size in ships.items():
for num in range(1, num_each_type + 1):
placed = False
while not placed:
row = random.randint(0, height - 1)
col = random.randint(0, width - 1)
is_vertical = random.choice([True, False])
if can_place_ship(board, row, col, size, is_vertical):
place_ship(board, row, col, size, is_vertical, ship_symbol)
placed = True
return board
def check_hit(board, row, col):
if 0 <= row < len(board) and 0 <= col < len(board[0]):
if board[row][col] not in ['.', 'O', 'X']:
board[row][col] = 'X'
return True
else:
if board[row][col] == '.':
board[row][col] = 'O'
return False
# Ships to be placed on the board
ships = {
'C': 5, # Carrier
'B': 4, # Battleship
'R': 3, # Cruiser
'S': 3, # Submarine
'D': 2 # Destroyer
}
Wrap the environment into a BattleshipBoard class#
# Define BattleshipBoard class
class BattleshipBoard:
def __init__(self, width, height, num_each_type=2, exclude_ships=[], init_with_one_hit=False):
self.width = width
self.height = height
self.ships = {s: ships[s] for s in ships if s not in exclude_ships}
self.board = create_and_fill_battleship_board(width, height, self.ships, num_each_type=num_each_type)
self.shots = [['.' for _ in range(width)] for _ in range(height)]
self.hits = 0
self.misses = 0
if init_with_one_hit:
initialized = False
for row in range(height):
for col in range(width):
if self.board[row][col] != '.':
self.check_shot(row, col)
initialized = True
break
if initialized:
break
def get_life_points(self):
return sum(self.ships.values())
def check_shot(self, row, col):
is_hit = check_hit(self.board, row, col)
if is_hit:
self.hits += 1
self.shots[row][col] = 'X'
else:
self.misses += 1
if self.shots[row][col] == '.':
self.shots[row][col] = 'O'
return is_hit
def check_terminate(self):
return (self.hits >= sum(self.ships.values())) or (self.misses + self.hits >= self.width * self.height)
def get_board(self):
return self.board
def get_shots(self):
return self.shots
def get_shots_overlay_board(self):
shots_overlay_board = [[self.board[row][col] if self.shots[row][col] == '.' else self.shots[row][col] for col in range(self.width)] for row in range(self.height)]
return shots_overlay_board
def get_hits(self):
return self.hits
def get_misses(self):
return self.misses
def get_game_status(self):
if self.hits == sum(self.ships.values()):
return 'Game Over: All ships sunk!'
return 'Game in progress'
def visualize_board(self):
str_rep = ''
for row in self.board:
str_rep += ' '.join(row) + '\n'
print(str_rep)
def visualize_own_board(self):
str_rep = ''
board = self.get_shots_overlay_board()
for row in board:
str_rep += ' '.join(row) + '\n'
print(str_rep)
def visualize_shots(self):
str_rep = ''
for row in self.shots:
str_rep += ' '.join(row) + '\n'
print(str_rep)
Define a Policy class with multiple trainable functions#
# Define Policy class
class Policy(Module):
def init(self, width, height):
pass
def __call__(self, map):
return self.select_coordinate(map).data
def select_coordinate(self, map):
plan = self.reason(map)
output = self.act(map, plan)
return output
@bundle(trainable=True)
def act(self, map, plan):
"""
Given a map, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
return
@bundle(trainable=True)
def reason(self, map):
"""
Given a map, analyze the board in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
return
Helper Functions to rollout and evaluate the policy#
# Function to get user feedback for placing shot
def user_fb_for_placing_shot(board, coords):
try:
reward = board.check_shot(coords[0], coords[1])
new_map = board.get_shots()
terminal = board.check_terminate()
return new_map, reward, terminal, f"Got {int(reward)} reward."
except Exception as e:
board.misses += 1
return board.get_shots(), 0, False, str(e)
# Function to rollout policy
def rollout(policy, board):
rewards = []
obs = board.get_shots()
while not board.check_terminate():
output = policy(obs)
obs, reward, terminal, feedback = user_fb_for_placing_shot(board, output)
if terminal:
break
rewards.append(reward)
rewards = np.array(rewards)
return rewards
# Function to evaluate policy
def eval_policy(policy, board_size, num_each_type, exclude_ships, n_eval_episodes):
scores = []
for _ in range(n_eval_episodes):
board = BattleshipBoard(board_size, board_size, num_each_type=num_each_type, exclude_ships=exclude_ships)
rewards = rollout(policy, board)
scores.append(rewards.mean())
scores = np.array(scores)
print(f"Scores: {scores.mean()} ({scores.std()})")
return scores
Creating the initial policy#
Finally, create a Policy object and evaluate the performance of the initial code.
# Set parameters
board_size = 5
num_each_type = 1
exclude_ships = ['C']
n_eval_episodes = 3
# Create policy and evaluate
policy = Policy()
init_scores = eval_policy(policy, board_size, num_each_type, exclude_ships, n_eval_episodes)
print("Initial scores:", init_scores)
Scores: 0.0 (0.0)
Initial scores: [0. 0. 0.]
Putting it all together#
Create an optimizer and evaluate the online optimization of the produced codes.
optimizer = OptoPrime(policy.parameters(), memory_size=0, config_list=config_list_from_json("OAI_CONFIG_LIST"))
feedback = ""
# This is an online optimization problem. we have the opportunity to
# keep changing the function with each round of interaction
board = BattleshipBoard(board_size, board_size, num_each_type=num_each_type, exclude_ships=exclude_ships)
obs = node(board.get_shots()) # init observation
i = 0
while i < 4:
GRAPH.clear()
try:
output = policy.select_coordinate(obs)
obs, reward, terminal, feedback = user_fb_for_placing_shot(board, output.data) # not traced
except ExecutionError as e: # this is a retry
output = e.exception_node
feedback = output.data
reward, terminal = 0, False
if terminal:
board = BattleshipBoard(board_size, board_size, num_each_type=num_each_type, exclude_ships=exclude_ships)
obs = node(board.get_shots()) # init observation
# Update
optimizer.zero_feedback()
optimizer.backward(output, feedback)
optimizer.step(verbose=True)
# Logging
if not isinstance(output, ExceptionNode):
try:
returns = eval_policy(policy, board_size, num_each_type, exclude_ships, n_eval_episodes)
print("Iteration", i, "returns:", returns)
except Exception:
pass
i += 1
Prompt
You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.
Specifically, a problem will be composed of the following parts:
- #Instruction: the instruction which describes the things you need to do or the question you should answer.
- #Code: the code defined in the problem.
- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.
- #Variables: the input variables that you can change.
- #Constraints: the constraints or descriptions of the variables in #Variables.
- #Inputs: the values of other inputs to the code, which are not changeable.
- #Others: the intermediate values created through the code execution.
- #Outputs: the result of the code output.
- #Feedback: the feedback about the code's execution result.
In #Variables, #Inputs, #Outputs, and #Others, the format is:
<data_type> <variable_name> = <value>
If <type> is (code), it means <value> is the source code of a python code, which may include docstring and definitions.
Output_format: Your output should be in the following json format, satisfying the json syntax:
{{
"reasoning": <Your reasoning>,
"answer": <Your answer>,
"suggestion": {{
<variable_1>: <suggested_value_1>,
<variable_2>: <suggested_value_2>,
}}
}}
In "reasoning", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.
If #Instruction asks for an answer, write it down in "answer".
If you need to suggest a change in the values of #Variables, write down the suggested values in "suggestion". Remember you can change only the values in #Variables, not others. When <type> of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.
If no changes or answer are needed, just output TERMINATE.
Now you see problem instance:
================================
#Instruction
You need to change the <value> of the variables in #Variables to improve the output in accordance to #Feedback.
#Code
eval0 = eval(self=self0, map=list0, __code=__code1)
eval1 = eval(self=self1, map=list0, plan=eval0, __code=__code0)
#Documentation
[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).
#Variables
(code) __code1:def reason(self, map):
"""
Given a map, analyze the board in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
return
(code) __code0:def act(self, map, plan):
"""
Given a map, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
return
#Constraints
(code) __code1: The code should start with:
def reason(self, map):
"""
Given a map, analyze the board in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
(code) __code0: The code should start with:
def act(self, map, plan):
"""
Given a map, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
#Inputs
(Policy) self1=<__main__.Policy object at 0x7f6d1d53d810>
(Policy) self0=<__main__.Policy object at 0x7f6d1d53d810>
(list) list0=[['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
#Others
(NoneType) eval0=None
#Outputs
(NoneType) eval1=None
#Feedback
'NoneType' object is not subscriptable
================================
Your response:
LLM response:
{
"reasoning": "In this problem, two evaluation functions (`eval0` and `eval1`) are defined using provided code strings (`__code1` and `__code0`). The feedback stating 'NoneType' object is not subscriptable indicates an issue where a `None` object is being treated as if it can be indexed like a list or a dictionary. This issue arises because both `__code1` and `__code0` functions do not return any value, i.e., they implicitly return `None`. When `eval1` expects to receive a 'plan' that can be accessed using indices, it instead receives `None` from `eval0`, which causes the error. To resolve this issue, both `__code1` and `__code0` functions need to be updated to return meaningful values to prevent the subscriptability error.",
"suggestion": {
"__code1": "def reason(self, map):\n \"\"\"\n Given a map, analyze the board in a game.\n X denotes hits, O denotes misses, and . denotes unknown positions.\n \"\"\"\n analysis_result = {'hits': 0, 'misses': 0, 'unknowns': 0}\n for row in map:\n for cell in row:\n if cell == 'X':\n analysis_result['hits'] += 1\n elif cell == 'O':\n analysis_result['misses'] += 1\n elif cell == '.':\n analysis_result['unknowns'] += 1\n return analysis_result",
"__code0": "def act(self, map, plan):\n \"\"\"\n Given a map and a plan, select a target coordinate in a game.\n X denotes hits, O denotes misses, and . denotes unknown positions.\n \"\"\"\n x, y = None, None\n for i, row in enumerate(map):\n for j, cell in enumerate(row):\n if cell == '.' and plan['unknowns'] > 0: # check for unknown spots as a sample strategy\n x, y = i, j\n break\n if x is not None:\n break\n return (x, y)"
}
}
Scores: 0.4788647342995169 (0.01701570386908862)
Iteration 0 returns: [0.5 0.47826087 0.45833333]
Prompt
You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.
Specifically, a problem will be composed of the following parts:
- #Instruction: the instruction which describes the things you need to do or the question you should answer.
- #Code: the code defined in the problem.
- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.
- #Variables: the input variables that you can change.
- #Constraints: the constraints or descriptions of the variables in #Variables.
- #Inputs: the values of other inputs to the code, which are not changeable.
- #Others: the intermediate values created through the code execution.
- #Outputs: the result of the code output.
- #Feedback: the feedback about the code's execution result.
In #Variables, #Inputs, #Outputs, and #Others, the format is:
<data_type> <variable_name> = <value>
If <type> is (code), it means <value> is the source code of a python code, which may include docstring and definitions.
Output_format: Your output should be in the following json format, satisfying the json syntax:
{{
"reasoning": <Your reasoning>,
"answer": <Your answer>,
"suggestion": {{
<variable_1>: <suggested_value_1>,
<variable_2>: <suggested_value_2>,
}}
}}
In "reasoning", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.
If #Instruction asks for an answer, write it down in "answer".
If you need to suggest a change in the values of #Variables, write down the suggested values in "suggestion". Remember you can change only the values in #Variables, not others. When <type> of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.
If no changes or answer are needed, just output TERMINATE.
Now you see problem instance:
================================
#Instruction
You need to change the <value> of the variables in #Variables to improve the output in accordance to #Feedback.
#Code
eval0 = eval(self=self0, map=map0, __code=__code1)
eval1 = eval(self=self1, map=map1, plan=eval0, __code=__code0)
#Documentation
[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).
#Variables
(code) __code1:def reason(self, map):
"""
Given a map, analyze the board in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
analysis_result = {'hits': 0, 'misses': 0, 'unknowns': 0}
for row in map:
for cell in row:
if cell == 'X':
analysis_result['hits'] += 1
elif cell == 'O':
analysis_result['misses'] += 1
elif cell == '.':
analysis_result['unknowns'] += 1
return analysis_result
(code) __code0:def act(self, map, plan):
"""
Given a map and a plan, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
x, y = None, None
for i, row in enumerate(map):
for j, cell in enumerate(row):
if cell == '.' and plan['unknowns'] > 0: # check for unknown spots as a sample strategy
x, y = i, j
break
if x is not None:
break
return (x, y)
#Constraints
(code) __code1: The code should start with:
def reason(self, map):
"""
Given a map, analyze the board in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
(code) __code0: The code should start with:
def act(self, map, plan):
"""
Given a map, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
#Inputs
(Policy) self1=<__main__.Policy object at 0x7f6d1d53d810>
(list) map1=[['O', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
(Policy) self0=<__main__.Policy object at 0x7f6d1d53d810>
(list) map0=[['O', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
#Others
(dict) eval0={'hits': 0, 'misses': 0, 'unknowns': 25}
#Outputs
(tuple) eval1=(0, 0)
#Feedback
Got 0 reward.
================================
Your response:
LLM response:
{
"reasoning": "The code provided involves two primary functions defined under __code1 and __code0. The first function (__code1) is designed to analyze a game board represented by a map and identify hits ('X'), misses ('O'), and unknown positions ('.'). According to the execution results, this function performs correctly as it counts 24 unknown positions. The second function (__code0) is intended to act based on the map and the analysis provided by __code1 to select a target coordinate on the board, most likely for making a move in the game. It selects the first unknown position on the map it encounters, which is (0,1). However, the 'Got 0 reward' feedback implies that either the target selection was not optimal for receiving a reward or another aspect of the game strategy needs adjustment. The issue seems to revolve around the response strategy to the game's situation rather than the implementation of target selection because the function indeed selected a legitimate unknown position on the board. The decision-making aspect, especially considering strategic placement or selection beyond the first viable option, could be where improvements are needed. Nevertheless, the code explicitly targets the first unknown cell when unknowns are present, which, as per current instructions and the functioning of __code0, is performed as expected. Thus, no changes seem necessary to the selections made by __code0.",
"answer": "",
"suggestion": {}
}
Scores: 0.6344540280467054 (0.15523541800098087)
Iteration 1 returns: [0.84615385 0.47826087 0.57894737]
Prompt
You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.
Specifically, a problem will be composed of the following parts:
- #Instruction: the instruction which describes the things you need to do or the question you should answer.
- #Code: the code defined in the problem.
- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.
- #Variables: the input variables that you can change.
- #Constraints: the constraints or descriptions of the variables in #Variables.
- #Inputs: the values of other inputs to the code, which are not changeable.
- #Others: the intermediate values created through the code execution.
- #Outputs: the result of the code output.
- #Feedback: the feedback about the code's execution result.
In #Variables, #Inputs, #Outputs, and #Others, the format is:
<data_type> <variable_name> = <value>
If <type> is (code), it means <value> is the source code of a python code, which may include docstring and definitions.
Output_format: Your output should be in the following json format, satisfying the json syntax:
{{
"reasoning": <Your reasoning>,
"answer": <Your answer>,
"suggestion": {{
<variable_1>: <suggested_value_1>,
<variable_2>: <suggested_value_2>,
}}
}}
In "reasoning", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.
If #Instruction asks for an answer, write it down in "answer".
If you need to suggest a change in the values of #Variables, write down the suggested values in "suggestion". Remember you can change only the values in #Variables, not others. When <type> of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.
If no changes or answer are needed, just output TERMINATE.
Now you see problem instance:
================================
#Instruction
You need to change the <value> of the variables in #Variables to improve the output in accordance to #Feedback.
#Code
eval0 = eval(self=self0, map=map0, __code=__code1)
eval1 = eval(self=self1, map=map1, plan=eval0, __code=__code0)
#Documentation
[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).
#Variables
(code) __code1:def reason(self, map):
"""
Given a map, analyze the board in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
analysis_result = {'hits': 0, 'misses': 0, 'unknowns': 0}
for row in map:
for cell in row:
if cell == 'X':
analysis_result['hits'] += 1
elif cell == 'O':
analysis_result['misses'] += 1
elif cell == '.':
analysis_result['unknowns'] += 1
return analysis_result
(code) __code0:def act(self, map, plan):
"""
Given a map and a plan, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
x, y = None, None
for i, row in enumerate(map):
for j, cell in enumerate(row):
if cell == '.' and plan['unknowns'] > 0: # check for unknown spots as a sample strategy
x, y = i, j
break
if x is not None:
break
return (x, y)
#Constraints
(code) __code1: The code should start with:
def reason(self, map):
"""
Given a map, analyze the board in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
(code) __code0: The code should start with:
def act(self, map, plan):
"""
Given a map, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
#Inputs
(Policy) self1=<__main__.Policy object at 0x7f6d1d53d810>
(list) map1=[['O', 'O', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
(Policy) self0=<__main__.Policy object at 0x7f6d1d53d810>
(list) map0=[['O', 'O', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
#Others
(dict) eval0={'hits': 0, 'misses': 1, 'unknowns': 24}
#Outputs
(tuple) eval1=(0, 1)
#Feedback
Got 0 reward.
================================
Your response:
LLM response:
{
"reasoning": "The feedback 'Got 0 reward' implies that the output from the code, which calculates a move, could potentially not advance the player significantly in the game objective. Generally, actions that are significant, such as hitting a target that changes the game state, would elicit a positive reward. Given the code, we evaluate the game map and make decisions based on that evaluation. The function '__code1' is designed to count the hits, misses, and unknown positions on a game board. This evaluation was given in 'Others' and it appears to be correctly implemented with the output '{'hits': 0, 'misses': 1, 'unknowns': 24}'. Therefore, the reasoning part isn't necessarily flawed. However, '__code0' that intends to specify the next action based on the evaluation could be problematic. The current strategy is to select the first unknown position found. In a strategy game, often the placement or choice of next move should be strategic and may not just rely on finding the first available unknown position but perhaps on a more sophisticated decision-making process. Given this, the algorithm lacks a deeper game strategy and changing the logic in '__code0' might lead to better gaming outcomes or rewards.",
"suggestion": {
"__code0": "def act(self, map, plan):\n \"\"\"\n Given a map and a plan, select a target coordinate in a game.\n X denotes hits, O denotes misses, and . denotes unknown positions.\n This version prioritizes targeting unknown cells near known misses as they are likely next to hits in strategic game scenarios like Battleship.\n \"\"\"\n best_option = None\n best_score = -1\n\n for i, row in enumerate(map):\n for j, cell in enumerate(row):\n if cell == '.' and (i > 0 and map[i-1][j] == 'O' or\n i < len(map) - 1 and map[i+1][j] == 'O' or\n j > 0 and map[i][j-1] == 'O' or\n j < len(row) - 1 and map[i][j+1] == 'O'):\n score = 1 # increase score based on proximity to a miss which might edge near a hit\n if score > best_score:\n best_score = score\n best_option = (i, j)\n\n if best_option is not None:\n return best_option\n else:\n # fallback to the first unknown if no strategic selections are found\n for i, row in enumerate(map):\n for j, cell in enumerate(row):\n if cell == '.':\n return (i, j)\n return None"
}
}
Scores: 0.4888888888888889 (0.04321208107251127)
Iteration 2 returns: [0.45833333 0.55 0.45833333]
Prompt
You're tasked to solve a coding/algorithm problem. You will see the instruction, the code, the documentation of each function used in the code, and the feedback about the execution result.
Specifically, a problem will be composed of the following parts:
- #Instruction: the instruction which describes the things you need to do or the question you should answer.
- #Code: the code defined in the problem.
- #Documentation: the documentation of each function used in #Code. The explanation might be incomplete and just contain high-level description. You can use the values in #Others to help infer how those functions work.
- #Variables: the input variables that you can change.
- #Constraints: the constraints or descriptions of the variables in #Variables.
- #Inputs: the values of other inputs to the code, which are not changeable.
- #Others: the intermediate values created through the code execution.
- #Outputs: the result of the code output.
- #Feedback: the feedback about the code's execution result.
In #Variables, #Inputs, #Outputs, and #Others, the format is:
<data_type> <variable_name> = <value>
If <type> is (code), it means <value> is the source code of a python code, which may include docstring and definitions.
Output_format: Your output should be in the following json format, satisfying the json syntax:
{{
"reasoning": <Your reasoning>,
"answer": <Your answer>,
"suggestion": {{
<variable_1>: <suggested_value_1>,
<variable_2>: <suggested_value_2>,
}}
}}
In "reasoning", explain the problem: 1. what the #Instruction means 2. what the #Feedback on #Output means to #Variables considering how #Variables are used in #Code and other values in #Documentation, #Inputs, #Others. 3. Reasoning about the suggested changes in #Variables (if needed) and the expected result.
If #Instruction asks for an answer, write it down in "answer".
If you need to suggest a change in the values of #Variables, write down the suggested values in "suggestion". Remember you can change only the values in #Variables, not others. When <type> of a variable is (code), you should write the new definition in the format of python code without syntax errors, and you should not change the function name or the function signature.
If no changes or answer are needed, just output TERMINATE.
Now you see problem instance:
================================
#Instruction
You need to change the <value> of the variables in #Variables to improve the output in accordance to #Feedback.
#Code
eval0 = eval(self=self0, map=map0, __code=__code1)
eval1 = eval(self=self1, map=map1, plan=eval0, __code=__code0)
#Documentation
[eval] This operator eval(__code, *args, **kwargs) evaluates the code block, where __code is the code (str) and *args and **kwargs are the arguments of the function. The output is the result of the evaluation, i.e., __code(*args, **kwargs).
#Variables
(code) __code1:def reason(self, map):
"""
Given a map, analyze the board in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
analysis_result = {'hits': 0, 'misses': 0, 'unknowns': 0}
for row in map:
for cell in row:
if cell == 'X':
analysis_result['hits'] += 1
elif cell == 'O':
analysis_result['misses'] += 1
elif cell == '.':
analysis_result['unknowns'] += 1
return analysis_result
(code) __code0:def act(self, map, plan):
"""
Given a map and a plan, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
This version prioritizes targeting unknown cells near known misses as they are likely next to hits in strategic game scenarios like Battleship.
"""
best_option = None
best_score = -1
for i, row in enumerate(map):
for j, cell in enumerate(row):
if cell == '.' and (i > 0 and map[i-1][j] == 'O' or
i < len(map) - 1 and map[i+1][j] == 'O' or
j > 0 and map[i][j-1] == 'O' or
j < len(row) - 1 and map[i][j+1] == 'O'):
score = 1 # increase score based on proximity to a miss which might edge near a hit
if score > best_score:
best_score = score
best_option = (i, j)
if best_option is not None:
return best_option
else:
# fallback to the first unknown if no strategic selections are found
for i, row in enumerate(map):
for j, cell in enumerate(row):
if cell == '.':
return (i, j)
return None
#Constraints
(code) __code1: The code should start with:
def reason(self, map):
"""
Given a map, analyze the board in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
(code) __code0: The code should start with:
def act(self, map, plan):
"""
Given a map, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions.
"""
#Inputs
(Policy) self1=<__main__.Policy object at 0x7f6d1d53d810>
(list) map1=[['O', 'O', 'X', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
(Policy) self0=<__main__.Policy object at 0x7f6d1d53d810>
(list) map0=[['O', 'O', 'X', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
#Others
(dict) eval0={'hits': 0, 'misses': 2, 'unknowns': 23}
#Outputs
(tuple) eval1=(0, 2)
#Feedback
Got 1 reward.
================================
Your response:
LLM response:
{
"reasoning": "Looking at the code and the constraints, the __code1 function which is defined for analyzing the map correctly identified only two misses instead of three, and no hits recognized. The __code0 function for acting on the map is supposed to prioritize targeting unknown cells near misses. However, there seems to be no issues with the __code0 as the output (0, 2) correctly attempts to target a cell adjacent to a known miss for likely strategic advantage in games like Battleship, as the 'X' which denotes a hit is nearby. Both functions have constraints on their structure but the definition of how they process elements is not restricted. Thus, the main issue lies in __code1 where the hits count is zero, which implies the loop might be missing cases or not progressing properly through every cell.",
"answer": null,
"suggestion": {
"__code1": "def reason(self, map):\n \"\"\"\n Given a map, analyze the board in a game.\n X denotes hits, O denotes misses, and . denotes unknown positions.\n \"\"\"\n analysis_result = {'hits': 0, 'misses': 0, 'unknowns': 0}\n for row in map:\n for cell in row:\n if cell == 'X':\n analysis_result['hits'] += 1\n elif cell == 'O':\n analysis_result['misses'] += 1\n elif cell == '.':\n analysis_result['unknowns'] += 1\n return analysis_result"
}
}
Scores: 0.4788647342995169 (0.01701570386908862)
Iteration 3 returns: [0.47826087 0.5 0.45833333]
This completes the tutorial on using the Trace package for optimizing multiple codes in an episodic setting. Happy optimizing!