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
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
Policy.reason0 = eval(self=Policy0, map=list150, __code=__code1)
Policy.act0 = eval(self=Policy1, map=list150, plan=Policy.reason0, __code=__code0)
#Documentation
[Policy.act] Given a map, select a target coordinate in a game.
X denotes hits, O denotes misses, and . denotes unknown positions..
#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) Policy1=<__main__.Policy object at 0x000002271BB641F0>
(Policy) Policy0=<__main__.Policy object at 0x000002271BB641F0>
(list) list150=[['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
#Others
(NoneType) Policy.reason0=None
#Outputs
(NoneType) Policy.act0=None
#Feedback:
'NoneType' object is not subscriptable
================================
Your response:
LLM response:
{
"reasoning": "The issue here is that the provided functions '__code1' and '__code0' for 'reason' and 'act' methods respectively are empty and return nothing (implicitly None). This becomes problematic when 'Policy.act0' tries to use the result of 'Policy.reason0' as part of its logic. If 'Policy.reason0' is None, it can't be used in the expected manner by 'Policy.act0', which likely attempts to subscript it or perform some operation that is not valid on a NoneType, leading to the 'NoneType' object is not subscriptable error. To improve the output, the '__code1' and '__code0' functions need to be correctly implemented so that 'reason' returns a valid plan that 'act' can use. For instance, 'reason' should analyze the given map to determine a strategic action plan (e.g., a coordinate to hit next), and 'act' should take this plan and execute a specific action based on it.",
"answer": "",
"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 # Example implementation:\n # Analyze the map to find the first unknown position\n for i, row in enumerate(map):\n for j, cell in enumerate(row):\n if cell == '.':\n return (i, j)\n return None",
"__code0": "def act(self, map, plan):\n \"\"\"\n Given a map, select a target coordinate in a game.\n X denotes hits, O denotes misses, and . denotes unknown positions.\n \"\"\"\n # Example implementation:\n # Return the plan as is, assuming it's a valid target\n return plan\n"
}
}
Scores: 0.4801587301587302 (0.030865772194650906)
Iteration 0 returns: [0.45833333 0.45833333 0.52380952]
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=Policy0, map=list0, __code=__code1)
eval1 = eval(self=Policy1, map=list1, 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.
"""
# Example implementation:
# Analyze the map to find the first unknown position
for i, row in enumerate(map):
for j, cell in enumerate(row):
if cell == '.':
return (i, j)
return None
(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.
"""
# Example implementation:
# Return the plan as is, assuming it's a valid target
return plan
#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) Policy1=<__main__.Policy object at 0x000002271BB641F0>
(list) list1=[['X', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
(Policy) Policy0=<__main__.Policy object at 0x000002271BB641F0>
(list) list0=[['X', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
#Others
(tuple) eval0=(0, 0)
#Outputs
(tuple) eval1=(0, 0)
#Feedback:
Got 1 reward.
================================
Your response:
LLM response:
{
"reasoning": "According to the instruction, we need to improve the output based on the feedback which states 'Got 1 reward'. This would suggest that the actions taken are correct but possibly not optimal, given the context provided is not sufficiently detailed regarding how rewards are obtained (e.g., hitting targets in a game represented by a grid). The usage of the eval() function in the code executes defined logic within __code1 and __code0 blocks for analyzing a game board and selecting a target coordinate, respectively. The __code1 block is correctly identifying the first unknown ('.') position, which is (0, 1) but returns (0, 0) mistakenly due to the incorrect starting index of eval0 in #Others. The __code0 block simply returns the 'plan' (a coordinate), demonstrating it's assumed to be a valid next action but seems misaligned with the intent of dynamically selecting the next strategy based on map analysis. To align the output with what might be considered a better strategic move or to improve the obtained reward from the action, adjustments are suggested in the logic for __code1 (initial analysis) and potentially __code nd __code1 (1, retrieving it correctly from the game analysis) while ensuring no syntax errors are introduced and the code suggestions abide by the given constraints.",
"answer": "",
"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 # Example implementation:\n # Analyze the map to find the first unknown position\n for i, row in enumerate(map):\n for j, cell in enumerate(row):\n if cell == '.':\n return (i, j)\n return None",
"__code0": "def act(self, map, plan):\n \"\"\"\n Given a map, select a target coordinate in a game.\n X denotes hits, O denotes misses, and . denotes unknown positions.\n \"\"\"\n # Example implementation:\n # Check if the plan is a valid target, if not, select the first unknown ('.') position\n for i, row in enumerate(map):\n for j, cell in enumerate(row):\n if cell == '.':\n return (i, j)\n return plan"
}
}
Scores: 0.4716183574879227 (0.00939393066793724)
Iteration 1 returns: [0.45833333 0.47826087 0.47826087]
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=Policy0, map=list0, __code=__code1)
eval1 = eval(self=Policy1, map=list1, 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.
"""
# Example implementation:
# Analyze the map to find the first unknown position
for i, row in enumerate(map):
for j, cell in enumerate(row):
if cell == '.':
return (i, j)
return None
(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.
"""
# Example implementation:
# Check if the plan is a valid target, if not, select the first unknown ('.') position
for i, row in enumerate(map):
for j, cell in enumerate(row):
if cell == '.':
return (i, j)
return plan
#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) Policy1=<__main__.Policy object at 0x000002271BB641F0>
(list) list1=[['X', 'O', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
(Policy) Policy0=<__main__.Policy object at 0x000002271BB641F0>
(list) list0=[['X', 'O', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
#Others
(tuple) eval0=(0, 1)
#Outputs
(tuple) eval1=(0, 1)
#Feedback:
Got 0 reward.
================================
Your response:
LLM response:
{
"reasoning": "The issue seems to lie in the act function's logic. According to the instructions, the act function should select a target coordinate in a game where 'X' denotes hits, 'O' denotes misses, and '.' denotes unknown positions. The provided __code0 for the act function incorrectly assumes that the plan (eval0) is always a valid target, but it should actually check if the target is an unknown ('.') position, and if not, it should find the next available unknown position. In this case, since eval0 returns (0, 1), and the map shows that position (0, 2) is the first unknown after (0, 1), the act function should be returning (0, 2) to improve the game outcome and possibly get a reward. However, it incorrectly just checks for the first '.' and returns (0, 1), which is not the desired logic for selecting the next target.",
"answer": "",
"suggestion": {
"__code0": "def act(self, map, plan):\n \"\"\"\n Given a map, select a target coordinate in a game.\n X denotes hits, O denotes misses, and . denotes unknown positions.\n \"\"\"\n # Check if the plan points to an unknown position; otherwise, select the next.\n i, j = plan\n if map[i][j] == '.':\n return (i, j + 1)\n else:\n for x, row in enumerate(map):\n for y, cell in enumerate(row):\n if cell == '.':\n return (x, y)\n return plan"
}
}
Scores: 0.013888888888888888 (0.019641855032959652)
Iteration 2 returns: [0.04166667 0. 0. ]
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=Policy0, map=list0, __code=__code1)
eval1 = eval(self=Policy1, map=list1, 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.
"""
# Example implementation:
# Analyze the map to find the first unknown position
for i, row in enumerate(map):
for j, cell in enumerate(row):
if cell == '.':
return (i, j)
return None
(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.
"""
# Check if the plan points to an unknown position; otherwise, select the next.
i, j = plan
if map[i][j] == '.':
return (i, j + 1)
else:
for x, row in enumerate(map):
for y, cell in enumerate(row):
if cell == '.':
return (x, y)
return plan
#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) Policy1=<__main__.Policy object at 0x000002271BB641F0>
(list) list1=[['X', 'O', '.', 'O', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
(Policy) Policy0=<__main__.Policy object at 0x000002271BB641F0>
(list) list0=[['X', 'O', '.', 'O', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.'], ['.', '.', '.', '.', '.']]
#Others
(tuple) eval0=(0, 2)
#Outputs
(tuple) eval1=(0, 3)
#Feedback:
Got 0 reward.
================================
Your response:
LLM response:
{
"reasoning": "The instruction implies that the issue lies with the actions defined in __code1 and __code0. Given the maps and the evaluation output, it looks like the evaluation is not meeting the intended outcome, which is likely to find the next unknown position to target and get a reward. __code1 correctly identifies the first unknown position on the map, and eval0 reflects this with (0, 2). However, __code0 should select the target coordinate based on some criteria of success, but it currently just moves to the next coordinate from the plan given by eval0, without checking if the move makes strategic sense or leads to a reward. Given the feedback 'Got 0 reward', the move made by __code0 from (0, 2) to (0, 3) might not be an optimal strategy. It seems __code0 fails to define a strategic move after identifying the first unknown position. A better strategy might involve checking if moving to a certain position increases the chance of hitting a target ('X') or avoiding misses ('O').",
"answer": "",
"suggestion": {
"__code0": "def act(self, map, plan):\n \"\"\"\n Given a map, select a target coordinate in a game.\n X denotes hits, O denotes misses, and . denotes unknown positions.\n \"\"\"\n i, j = plan\n # Implement a smarter strategy to find the next best move.\n # This involves looking for the next '.' in a row wise manner.\n for x in range(i, len(map)):\n if '.' in map[x]:\n for y in range(len(map[x])):\n if map[x][y] == '.':\n return (x, y)\n return plan"
}
}
Scores: 0.4583333333333333 (0.0)
Iteration 3 returns: [0.45833333 0.45833333 0.45833333]
This completes the tutorial on using the Trace package for optimizing multiple codes in an episodic setting. Happy optimizing!