我正在致力于使用 OpenAI 的 Gym 为 Pong 游戏实现强化学习 (RL) 环境。目标是训练人工智能代理通过控制球拍来打乒乓球。
代理收到太多负面奖励，即使它看起来移动正确。具体来说，奖励函数会惩罚远离球的智能体，但这种情况发生得太频繁，即使球朝球拍移动时似乎也会发生。
观察空间：观察空间包括物体的位置和速度。球和球拍、球和球拍之间的距离以及球拍速度。
动作空间：动作空间由三个离散动作组成：向上移动、向下移动和保持静止。
奖励功能：奖励函数奖励智能体击球，并惩罚它错过球或远离球。
如何调整奖励函数，以仅在球向球移动时远离球时惩罚智能体桨？或者我的游戏逻辑或 DQNA 实施还有其他问题吗？任何有关提高训练性能的建议将不胜感激。
以下是我的 DQNA 代码：
import tensorflow as tf
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.optimizers import Adam
import numpy as np
import random
from collections import deque
class DQNAgent:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.memory = deque(maxlen=10000) # Increased replay memory size
self.gamma = 0.99 # Increased discount rate
self.epsilon = 1.0 # Exploration rate
self.epsilon_min = 0.01
self.epsilon_decay = 0.995 # Slower epsilon decay
self.learning_rate = 0.001 # Slightly increased learning rate
self.model = self._build_model()
self.target_model = self._build_model()
self.update_target_model()
def _build_model(self):
model = Sequential()
model.add(Input(shape=(self.state_size,))) # Added Input layer to define the input shape
model.add(Dense(48, activation='relu')) # Increased neurons to 48
model.add(Dropout(0.2)) # Dropout layer for regularization
model.add(Dense(48, activation='relu')) # Increased neurons to 48
model.add(Dropout(0.2)) # Dropout layer for regularization
model.add(Dense(48, activation='relu')) # Increased neurons to 48
model.add(Dropout(0.2)) # Dropout layer for regularization
model.add(Dense(48, activation='relu')) # Increased neurons to 48
model.add(Dropout(0.2)) # Dropout layer for regularization
model.add(Dense(48, activation='relu')) # Increased neurons to 48
model.add(Dropout(0.2)) # Dropout layer for regularization
model.add(Dense(48, activation='relu')) # Increased neurons to 48
model.add(Dropout(0.2)) # Dropout layer for regularization
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))
return model
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def act(self, state):
if np.random.rand() <= self.epsilon:
return np.random.randint(self.action_size)
else:
act_values = self.model.predict(state)
return np.argmax(act_values[0])
def replay(self, batch_size):
if len(self.memory) < batch_size:
return 0 # Not enough memory to sample
minibatch = random.sample(self.memory, batch_size)
total_loss = 0
for state, action, reward, next_state, done in minibatch:
target = reward
if not done:
target = (reward + self.gamma *
np.amax(self.target_model.predict(next_state)[0]))
target_f = self.model.predict(state)
target_f[0][action] = target
history = self.model.fit(state, target_f, epochs=1, verbose=0)
total_loss += history.history['loss'][0]
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
return total_loss / batch_size # Return average loss
def update_target_model(self):
# Copy weights from model to target_model
self.target_model.set_weights(self.model.get_weights())
def save(self, filename):
self.model.save(f"{filename}.keras")
def load(self, filename):
self.model = load_model(f"{filename}")
self.target_model = self._build_model()
self.update_target_model()
以下是我的 Pong 逻辑代码：
import gym
from gym import spaces
import numpy as np
import pygame
from pygame.locals import K_w, K_s
class PongEnv(gym.Env):
metadata = {'render.modes': ['human']}
def __init__(self):
super(PongEnv, self).__init__()
self.width = 640
self.height = 480
self.ball_speed = 7
self.paddle_speed = 12
self.ball = pygame.Rect(self.width // 2 - 15, self.height // 2 - 15, 30, 30)
self.player_paddle = pygame.Rect(self.width - 20, self.height // 2 - 70, 10, 140)
self.ai_paddle = pygame.Rect(10, self.height // 2 - 70, 10, 140)
self.ball_dx, self.ball_dy = self.ball_speed, self.ball_speed
self.player_paddle_speed = 0 # Initialize player paddle speed
self.action_space = spaces.Discrete(3)
self.observation_space = spaces.Box(low=0, high=255, shape=(13,), dtype=np.float32) # Updated shape
def reset(self):
self.ball.center = (self.width // 2, self.height // 2)
self.player_paddle.centery = self.height // 2
self.ai_paddle.centery = self.height // 2
self.ball_dx, self.ball_dy = self.ball_speed, self.ball_speed
self.player_paddle_speed = 0 # Reset player paddle speed
return self.get_state()
def get_state(self):
distance_ball_player = self.ball.centery - self.player_paddle.centery
distance_ball_ai = self.ball.centery - self.ai_paddle.centery
state = [
self.player_paddle.centery,
self.ai_paddle.centery,
self.ball.centerx,
self.ball.centery,
self.ball_dx,
self.ball_dy, # Adding ball's vertical speed
distance_ball_player, # Adding distance between ball and player paddle
distance_ball_ai, # Adding distance between ball and AI paddle
self.player_paddle.top,
self.player_paddle.bottom,
self.ai_paddle.top,
self.ai_paddle.bottom,
self.player_paddle_speed, # Adding player paddle speed
]
return np.array(state, dtype=np.float32)
def step(self, action):
reward = 0
done = False
previous_player_paddle_position = self.player_paddle.centery # Track previous position
# Move player paddle
if action == 0:
self.player_paddle.centery -= self.paddle_speed
elif action == 1:
self.player_paddle.centery += self.paddle_speed
# Ensure paddle stays within the screen
if self.player_paddle.top < 0:
self.player_paddle.top = 0
if self.player_paddle.bottom > self.height:
self.player_paddle.bottom = self.height
# Calculate player paddle speed
self.player_paddle_speed = self.player_paddle.centery - previous_player_paddle_position
# Move the ball
self.ball.x += self.ball_dx
self.ball.y += self.ball_dy
# Ball collision with top or bottom
if self.ball.top <= 0 or self.ball.bottom >= self.height:
self.ball_dy *= -1
# Ball collision with paddles
if self.ball.colliderect(self.player_paddle):
self.ball_dx *= -1
reward += 5 # Reward for hitting the ball with the paddle
print(f"Ball hit by player paddle. Reward: {reward}")
elif self.ball.colliderect(self.ai_paddle):
self.ball_dx *= -1
# Check for out of bounds
if self.ball.left <= 0:
done = True
reward += 10 # Reward for scoring
print(f"Ball out of left bounds. Reward: {reward}")
elif self.ball.right >= self.width:
done = True
reward -= 10 # Penalty for opponent scoring
print(f"Ball out of right bounds. Penalty: {reward}")
# AI paddle movement
if self.ai_paddle.centery < self.ball.centery:
self.ai_paddle.centery += self.paddle_speed
if self.ai_paddle.centery > self.ball.centery:
self.ai_paddle.centery -= self.paddle_speed
# Ensure AI paddle stays within the screen
if self.ai_paddle.top < 0:
self.ai_paddle.top = 0
if self.ai_paddle.bottom > self.height:
self.ai_paddle.bottom = self.height
# Reward for moving towards the ball and penalty for moving away
if self.ball_dx > 0: # Ball moving towards the player paddle
if (action == 0 and self.player_paddle.centery > self.ball.centery) or (action == 1 and self.player_paddle.centery < self.ball.centery):
reward += 0.5
print(f"Player paddle moving towards ball. Reward: {reward}")
else:
reward -= 0.5 # Penalty for moving away from the ball
print(f"Player paddle moving away from ball. Penalty: {reward}")
next_state = self.get_state()
return next_state, reward, done, {}
def render(self, mode='human'):
if mode == 'human':
pygame.init()
screen = pygame.display.set_mode((self.width, self.height))
screen.fill((0, 0, 0))
pygame.draw.rect(screen, (255, 255, 255), self.player_paddle)
pygame.draw.rect(screen, (255, 255, 255), self.ai_paddle)
pygame.draw.ellipse(screen, (255, 255, 255), self.ball)
pygame.draw.aaline(screen, (255, 255, 255), (self.width // 2, 0), (self.width // 2, self.height))
pygame.display.flip()
你的代码有一些可以改进的地方，可以提高训练性能。以下是对代码和可能调整的详细分析：
1. 奖励函数
你关于奖励函数惩罚代理远离球的观察是正确的，即使它朝着球拍移动。这可能是由于奖励函数的设计造成的。目前，当球朝着玩家球拍移动时，它会奖励朝着球移动，并惩罚远离球的移动。但是，这种奖励策略可能并不总是有效，因为它没有考虑到球拍和球之间的距离。
让我们修改奖励函数，以更好地反映代理的行为：
# ... 在 PongEnv 类中的 step 函数中 ...
# 奖励移动到球附近并惩罚远离球
if self.ball_dx > 0: # 球朝玩家球拍移动
distance_before = abs(self.ball.centery - self.player_paddle.centery)
distance_after = abs(self.ball.centery - (self.player_paddle.centery + self.player_paddle_speed))
if distance_after < distance_before:
reward += 0.5 # 奖励靠近球
print(f"玩家球拍靠近球。奖励：{reward}")
else:
reward -= 0.5 # 惩罚远离球
print(f"玩家球拍远离球。惩罚：{reward}")
在这个修改后的奖励函数中，我们只在球拍实际靠近球时才给予奖励，而在球拍远离球时给予惩罚，而不管球拍的移动方向如何。这有助于代理学习更好地定位自身以击球。
2. 状态表示
你的状态表示非常全面，包括球的位置和速度、球拍的位置和球拍的速度。但是，你还可以通过添加派生特征（例如球和球拍之间的相对距离）来尝试进一步增强它。这可以帮助代理更容易地理解游戏状态。
3. DQNAgent 超参数
-
增加回放内存大小： 你的回放内存大小为 10000。你可以尝试将其增加到更大的值，例如 100000 或更多。更大的回放内存可以存储更多经验，并有助于打破经验之间的相关性，从而提高训练稳定性。
-
epsilon 衰减率： 你的 epsilon 衰减率为 0.995。这可能是一个相当快的衰减率。你可以尝试使用更慢的衰减率，例如 0.999 或 0.9995。这将使代理在更长的时间内探索更多，这可能有助于找到更好的策略。
-
学习率： 你的学习率为 0.001。虽然这个学习率是一个合理的起点，但你可以尝试一些不同的值，例如 0.0001 或 0.00005。较低的学习率可能有助于提高训练稳定性，但可能需要更多的时间来收敛。
-
目标网络更新频率： 你没有在代码中包含更新目标网络的频率。建议定期更新目标网络，例如每 1000 或 5000 步更新一次。这有助于稳定训练过程。
-
批量大小： 你可以尝试不同的批量大小，例如 32 或 64。
4. 网络架构
你的网络架构合理。但是，你可以尝试一些不同的激活函数，例如 LeakyReLU 或 ELU，而不是 ReLU。此外，你可以尝试添加更多层或更改每层的单元数来查看是否有帮助。
def _build_model(self):
model = Sequential()
model.add(Input(shape=(self.state_size,)))
model.add(Dense(64, activation='relu')) # 调整单元数
model.add(Dense(64, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))
return model
5. 训练时间
训练 DQN 代理可能需要很长时间才能收敛。确保你已经训练了足够长的时间。
调试技巧
-
打印奖励和 epsilon 值： 在训练期间，打印奖励和 epsilon 值，以监控代理的进度。这可以帮助你识别任何潜在的问题，例如奖励函数没有按预期工作。
-
可视化代理： 定期渲染游戏以可视化代理的性能。这可以帮助你识别代理所犯的任何明显错误。
请记住，强化学习可能很棘手，通常需要大量的试验和错误才能获得良好的结果。尝试不同的超参数、奖励函数和网络架构，以找到最适合你的问题的组合。
标签：python,tensorflow,reinforcement-learning,dqn From： 78809166