Hi, I've implemented a GridWorld and I want you to go through my code and make few…

Here is an improved version of your GridWorldDeterministic class:

import gym
import numpy as np


class GridWorldDeterministic(gym.Env):

    def __init__(self):
        self.grid = np.zeros((4, 4))
        self._initialize_grid()

        self.observation_space = gym.spaces.Discrete(16)
        self.action_space = gym.spaces.Discrete(4)
        self.reward_range = (-5, 10)
        self.agent_pos = (0, 0)

    def _initialize_grid(self):
        """Populate the grid with -1, -5, and 10 values."""
        self.grid[1, 1] = -1
        self.grid[1, 2] = -1
        self.grid[1, 3] = 10
        self.grid[0, 1] = -5
        self.grid[2, 1] = -5

    def reset(self):
        self.agent_pos = (0, 0)
        return self._flatten_pos(self.agent_pos)

    def step(self, action):
        x, y = self.agent_pos

        x, y = self._apply_action(action, x, y)

        # Ensure agent does not move outside the grid boundaries
        x, y = self._clip_pos(x, y)

        reward = self.grid[x, y] if self.grid[x, y] in (-5, 10) else -1
        next_state = self._flatten_pos((x, y))
        done = self.grid[x, y] == 10

        self.agent_pos = (x, y)
        return next_state, reward, done, {}

    def _apply_action(self, action, x, y):
        """Returns new position after applying the action."""
        if action == 0:  # up
            x -= 1
        elif action == 1:  # down
            x += 1
        elif action == 2:  # left
            y -= 1
        elif action == 3:  # right
            y += 1
        return x, y

    def _clip_pos(self, x, y):
        """Ensure position does not move outside the grid boundaries."""
        return np.clip(x, 0, 3), np.clip(y, 0, 3)

    def _flatten_pos(self, pos):
        """Convert 2D position to 1D index."""
        row, column = pos
        return row * 4 + column

    def render(self):
        print(self.grid)
        print("Agent position:", self.agent_pos)

Here's a summary of the changes I made:

• Removed self.rewards attribute as it wasn't used.
• Moved the grid initialization to a separate _initialize_grid() method for better readability.
• Separated the action application, position clipping, and position flattening processes into their own methods, making the step() method more readable.
• Simplified reward calculation and moved it to a single line based on the grid value at the new position.

These modifications should make the code more robust, easier to understand, and more optimal.

Here's an implementation of DQN for your GridWorld environment:

import gym
import numpy as np
import random
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt


class DQNAgent:

    def __init__(self, state_size, action_size, memory_size, epsilon_decay):
        self.state_size = state_size
        self.action_size = action_size
        self.memory_size = memory_size
        self.memory = deque(maxlen=memory_size)
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = epsilon_decay
        self.learning_rate = 0.001
        self.gamma = 0.95
        self.train_freq = 1
        self.weight_save_frequency = 200

        self.model = self._build_model()

    def _build_model(self):
        model = Sequential()
        model.add(Dense(32, input_dim=self.state_size, activation='relu'))
        model.add(Dense(32, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=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 random.randrange(self.action_size)
        return np.argmax(self.model.predict(state))

    def experience_replay(self, batch_size):
        if len(self.memory) < batch_size:
            return

        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target += self.gamma * np.amax(self.model.predict(next_state))
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.train_on_batch(state, target_f)

    def train(self, env, episodes, batch_size):
        episode_rewards = []
        episode_epsilon = []

        for episode in range(1, episodes + 1):
            state = env.reset()
            state = np.reshape(state, [1, self.state_size])
            reward_total = 0

            while True:
                action = self.act(state)
                next_state, reward, done, _ = env.step(action)
                next_state = np.reshape(next_state, [1, self.state_size])
                self.remember(state, action, reward, next_state, done)

                reward_total += reward
                state = next_state

                self.experience_replay(batch_size)

                if done:
                    print(f"Episode: {episode}, Reward: {reward_total}, Epsilon: {self.epsilon}")
                    episode_rewards.append(reward_total)
                    episode_epsilon.append(self.epsilon)
                    break

            if self.epsilon > self.epsilon_min:
                self.epsilon *= self.epsilon_decay

            if episode % self.weight_save_frequency == 0:
                self.save_weights('dqn_weights.h5')

        return episode_rewards, episode_epsilon

    def evaluate(self, env):
        state = env.reset()
        state = np.reshape(state, [1, self.state_size])
        step = 0
        while True:
            step += 1
            action = np.argmax(self.model.predict(state))
            next_state, reward, done, _ = env.step(action)
            next_state = np.reshape(next_state, [1, self.state_size])
            state = next_state
            env.render()
            if done:
                print(f"Episode finished in {step} steps")
                break

    def save_weights(self, file_name):
        self.model.save_weights(file_name)

    def load_weights(self, file_name):
        self.model.load_weights(file_name)
        self.epsilon = 0


def plot_results(rewards, epsilons):
    plt.subplot(2, 1, 1)
    plt.plot(rewards)
    plt.title('Reward vs Episode')
    plt.subplot(2, 1, 2)
    plt.plot(epsilons)
    plt.title('Epsilon vs Episode')
    plt.xlabel('Episodes')
    plt.tight_layout()
    plt.show()


if __name__ == "__main__":
    env = GridWorldDeterministic()
    agent = DQNAgent(state_size=16, action_size=4, memory_size=2000, epsilon_decay=0.995)

    # Train
    rewards, epsilons = agent.train(env, episodes=1000, batch_size=32)
    plot_results(rewards, epsilons)

    # Load weights
    agent.load_weights('dqn_weights.h5')

    # Evaluate
    agent.evaluate(env)

This code will:

• Implement a simple DQN agent with experience replay and epsilon-greedy exploration.
• Train the agent on the GridWorld environment for a specified number of episodes.
• Save and load the model weights from an h5 file.
• Plot the reward and epsilon decay graphs using Matplotlib.
• Evaluate the agent's performance in the GridWorld environment.

The parameters can be adjusted within the DQNAgent class and the train method to tune the agent's performance.

I apologize for the error. I see the cause of the error is in the state representation. We should convert state index into one-hot vector representation. I have also removed the unnecessary test parameter and made changes in the 'act' function for evaluation. Here's the corrected version of the code:

import gym
import numpy as np
import random
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt


class DQNAgent:

    def __init__(self, state_size, action_size, memory_size, epsilon_decay):
        self.state_size = state_size
        self.action_size = action_size
        self.memory_size = memory_size
        self.memory = deque(maxlen=memory_size)
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = epsilon_decay
        self.learning_rate = 0.001
        self.gamma = 0.95
        self.train_freq = 1
        self.weight_save_frequency = 200

        self.model = self._build_model()

    def _build_model(self):
        model = Sequential()
        model.add(Dense(32, input_dim=self.state_size, activation='relu'))
        model.add(Dense(32, activation='relu'))
        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 random.randrange(self.action_size)
        return np.argmax(self.model.predict(state))

    def experience_replay(self, batch_size):
        if len(self.memory) < batch_size:
            return

        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target += self.gamma * np.amax(self.model.predict(next_state))
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.train_on_batch(state, target_f)

    def state_to_onehot(self, state):
        return np.identity(self.state_size)[state].reshape(1, -1)

    def train(self, env, episodes, batch_size):
        episode_rewards = []
        episode_epsilon = []

        for episode in range(1, episodes + 1):
            state = env.reset()
            state = self.state_to_onehot(state)
            reward_total = 0

            while True:
                action = self.act(state)
                next_state, reward, done, _ = env.step(action)
                next_state = self.state_to_onehot(next_state)
                self.remember(state, action, reward, next_state, done)

                reward_total += reward
                state = next_state

                self.experience_replay(batch_size)

                if done:
                    print(f"Episode: {episode}, Reward: {reward_total}, Epsilon: {self.epsilon}")
                    episode_rewards.append(reward_total)
                    episode_epsilon.append(self.epsilon)
                    break

            if self.epsilon > self.epsilon_min:
                self.epsilon *= self.epsilon_decay

            if episode % self.weight_save_frequency == 0:
                self.save_weights('dqn_weights.h5')

        return episode_rewards, episode_epsilon

    def evaluate(self, env):
        state = env.reset()
        state = self.state_to_onehot(state)
        step = 0
        while True:
            step += 1
            action = np.argmax(self.model.predict(state))
            next_state, reward, done, _ = env.step(action)
            next_state = self.state_to_onehot(next_state)
            state = next_state
            env.render()
            if done:
                print(f"Episode finished in {step} steps")
                break

    def save_weights(self, file_name):
        self.model.save_weights(file_name)

    def load_weights(self, file_name):
        self.model.load_weights(file_name)
        self.epsilon = 0


def plot_results(rewards, epsilons):
    plt.subplot(2, 1, 1)
    plt.plot(rewards)
    plt.title('Reward vs Episode')
    plt.subplot(2, 1, 2)
    plt.plot(epsilons)
    plt.title('Epsilon vs Episode')
    plt.xlabel('Episodes')
    plt.tight_layout()
    plt.show()


if __name__ == "__main__":
    env = GridWorldDeterministic()
    agent = DQNAgent(state_size=16, action_size=4, memory_size=2000, epsilon_decay=0.995)

    # Train
    rewards, epsilons = agent.train(env, episodes=1000, batch_size=32)
    plot_results(rewards, epsilons)

    # Load weights
    agent.load_weights('dqn_weights.h5')

    # Evaluate
    agent.evaluate(env)

With these changes, the code should work properly. The 'evaluate' function now doesn't take any additional parameters and uses learned policy for agent evaluation. The state index is converted to a one-hot vector, which fixes the reshape error.

These lines are being printed by TensorFlow as a result of the model.train_on_batch function, and as you mentioned, using verbose doesn't seem to affect it. To suppress these messages and keep your output clean, you can set the logging level of TensorFlow as follows:

1. At the top of your script, import the tensorflow module and the logging module:

import tensorflow as tf
import logging

2. After importing the modules, disable the TensorFlow logging messages by setting the logging level:

tf.get_logger().setLevel(logging.ERROR)

Now, when you run the script, you shouldn't see those lines in the output. Here's an updated version of the entire script with these changes:

import gym
import numpy as np
import random
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import tensorflow as tf
import logging

tf.get_logger().setLevel(logging.ERROR)


class DQNAgent:

    # ... (unchanged code)

    def _build_model(self):
        model = Sequential()
        model.add(Dense(32, input_dim=self.state_size, activation='relu'))
        model.add(Dense(32, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))
        return model

    # ... (rest of the unchanged code)


if __name__ == "__main__":
    env = GridWorldDeterministic()
    agent = DQNAgent(state_size=16, action_size=4, memory_size=2000, epsilon_decay=0.995)

    # Train
    rewards, epsilons = agent.train(env, episodes=1000, batch_size=32)
    plot_results(rewards, epsilons)

    # Load weights
    agent.load_weights('dqn_weights.h5')

    # Evaluate
    agent.evaluate(env)

This should help suppress the logging messages you mentioned.

I have made the necessary changes to adapt the provided DQN implementation to the GridWorld environment and added experience replay and target network. Also, I've updated the training loop to record the number of steps taken in each episode as an additional metric for evaluating agent performance. Here's the updated code:

import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from collections import deque
import random

class DQNAgentPytorch:
    def __init__(self, env, memory_size=2000, epsilon=1.0, gamma=0.99, learning_rate=0.001):
        self.env = env
        self.observation_space = env.observation_space
        self.action_space = env.action_space
        self.memory = deque(maxlen=memory_size)
        self.epsilon = epsilon
        self.gamma = gamma
        self.lr = learning_rate
        self.model = self.build_model()
        self.target_model = self.build_model()
        self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
        self.criterion = nn.MSELoss()
        self.update_target_net()

    def build_model(self):
        model = nn.Sequential(
            nn.Linear(self.observation_space.n, 16),
            nn.ReLU(),
            nn.Linear(16, 16),
            nn.ReLU(),
            nn.Linear(16, self.action_space.n)
        )
        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def choose_action(self, state, test=False):
        if test or random.random() > self.epsilon:
            with torch.no_grad():
                state_oh = self.state_to_onehot(state)
                q_values = self.model(state_oh)
                return torch.argmax(q_values).item()
        else:
            return np.random.randint(self.action_space.n)

    def state_to_onehot(self, state):
        return torch.tensor(np.identity(self.observation_space.n)[state].reshape(1, -1), dtype=torch.float32)

    def train(self, batch_size):
        if len(self.memory) < batch_size:
            return

        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = self.target_model(self.state_to_onehot(state)).squeeze(0)
            if done:
                target[action] = reward
            else:
                next_q_values = self.target_model(self.state_to_onehot(next_state)).squeeze(0)
                target[action] = reward + self.gamma * torch.max(next_q_values)

            inputs = self.state_to_onehot(state)
            targets = target.unsqueeze(0)

            self.optimizer.zero_grad()

            outputs = self.model(inputs)
            loss = self.criterion(outputs, targets)
            loss.backward()

            self.optimizer.step()

    def update_target_net(self):
        self.target_model.load_state_dict(self.model.state_dict())

    def update_epsilon(self, new_epsilon):
        self.epsilon = new_epsilon

    def save_weights(self, file_name):
        torch.save(self.model.state_dict(), file_name)

    def load_weights(self, file_name):
        self.epsilon = 0
        self.model.load_state_dict(torch.load(file_name))


def train_dqn(agent, episodes, batch_size, epsilon_decay=0.995, min_epsilon=0.01):
    rewards = []
    steps_list = []
    epsilons = []
    target_update_freq = 25

    for episode_i in range(1, episodes + 1):
        state = agent.env.reset()
        done = False
        episode_reward = 0
        steps = 0
        while not done:
            steps += 1
            action = agent.choose_action(state)
            next_state, reward, done, _ = agent.env.step(action)
            agent.remember(state, action, reward, next_state, done)
            agent.train(batch_size)

            state = next_state
            episode_reward += reward

            if steps % target_update_freq == 0:
                agent.update_target_net()

        rewards.append(episode_reward)
        steps_list.append(steps)
        epsilons.append(agent.epsilon)
        agent.update_epsilon(max(agent.epsilon * epsilon_decay, min_epsilon))

        print(f"Episode: {episode_i}, Total Reward: {episode_reward}, Epsilon: {agent.epsilon}, Steps: {steps}")

    return rewards, epsilons, steps_list


env = GridWorldDeterministic()
agent = DQNAgentPytorch(env)
rewards, epsilons, steps_list = train_dqn(agent, episodes=500, batch_size=32)

agent.save_weights('dqn_gridworld_pytorch.h5')

# Plot reward graph
plt.plot(rewards)
plt.xlabel('Episodes')
plt.ylabel('Rewards')
plt.title('DQN GridWorld Rewards')
plt.grid()
plt.show()

# Plot epsilon decay graph
plt.plot(epsilons)
plt.xlabel('Episodes')
plt.ylabel('Epsilon')
plt.title('Epsilon Decay')
plt.grid()
plt.show()

# plot number of steps per episode
plt.plot(steps_list)
plt.xlabel('Episodes')
plt.ylabel('Steps')
plt.title('Number of steps per episode')
plt.grid()
plt.show()

The provided DQN agent has been updated to use one-hot encoding for the states and is now compatible with the GridWorld environment. I've also implemented experience replay and target networks to improve training stability. The training function has been modified to return the number of steps taken in each episode as well.

Please let me know if there's anything more you'd like me to add or modify!

I have made the necessary changes to the test_agent function to work with the latest DQNAgentPytorch implementation. Here's the modified code:

def test_agent(agent, env, episodes=5, max_steps=10):
    test_rewards = []
    
    for episode_i in range(1, episodes + 1):
        state = env.reset()
        done = False
        total_reward = 0
        step = 0

        while not done and step < max_steps:
            action = agent.choose_action(state, test=True)
            next_state, reward, done, _ = env.step(action)
            env.render()
            print("Action taken:", action)
            print("New state:", next_state)
            print("Reward received:", reward)
            print("Done?", done)
            print("="*20)
            state = next_state
            total_reward += reward
            step += 1
        
        test_rewards.append(total_reward)
        print(f'Test Episode: {episode_i}, Total Reward: {total_reward}, Steps: {step}')
    
    return test_rewards


env = GridWorldDeterministic()
agent = DQNAgentPytorch(env)
agent.load_weights('dqn_gridworld_pytorch.h5')

test_rewards = test_agent(agent, env)

# Plot test reward graph
plt.plot(test_rewards)
plt.xlabel('Test Episodes')
plt.ylabel('Rewards')
plt.title('DQN GridWorld Test Rewards')
plt.grid()
plt.show()

Now the test_agent function should work as expected with the new DQNAgentPytorch implementation. It takes the trained agent and the GridWorld environment as input parameters and runs the specified number of test episodes, printing the agent's actions, states, rewards, and a visual representation of the environment at each step. The function also returns the total rewards for each test episode, which can be plotted using the provided code snippet.

It seems like you have pasted the same code snippet from the previous question. As I mentioned earlier, I've already made the necessary changes to the test_agent function to work with the latest DQNAgentPytorch implementation, and the code snippet provided above should work fine with the GridWorld environment.

However, if you encounter any issues or would like me to make further modifications, please let me know!