# Deep Reinforcement Learning Algorithms: DQN, A3C, PPO

Deep reinforcement learning (DRL) is an exciting field that combines deep learning with reinforcement learning to solve complex problems. This article will delve into three popular algorithms in the realm of DRL - Deep Q-Networks (DQN), Asynchronous Advantage Actor-Critic (A3C), and Proximal Policy Optimization (PPO). We'll explore their concepts, implementations, and visualize some plots to better understand how they work.

## Introduction

Deep reinforcement learning involves training an agent to make decisions based on its interactions with the environment. The goal is for the agent to learn a policy that maximizes cumulative rewards over time. DQN, A3C, and PPO are three widely used algorithms in this domain, each offering unique advantages for different types of problems.

## Deep Q-Networks (DQN)

Deep Q-Networks were introduced by Mnih et al. in their 2015 paper "Playing Atari with Deep Reinforcement Learning." DQN combines deep neural networks and the Q-learning algorithm to learn optimal policies for discrete action spaces.

### Implementation

The core idea behind DQN is to use a convolutional neural network (CNN) as a function approximator for the Q-function, which estimates the expected return of taking an action in a given state. The following Python code demonstrates how to implement a basic DQN agent using OpenAI Gym's CartPole environment:

```python
import gym
from keras.models import Sequential
from keras.layers import Dense, Flatten
from collections import deque
import numpy as np

# Define the DQN Agent class
class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)

        # Initialize the DQN model and target network models
        self.model = self._build_model()
        self.target_model = self._build_model()
        self.update_target_model()

    def _build_model(self):
        model = Sequential([
            Flatten(input_shape=(self.state_size,)),
            Dense(24),
            Activation('relu'),
            Dense(24),
            Activation('relu'),
            Dense(self.action_size)
        ])
        model.compile(loss='mse', optimizer=Adam())
        return model

    def update_target_model(self):
        self.target_model.set_weights(self.model.get_weights())

    # ... (Add more methods like remember, act, and train)
```

### Visualizing DQN Training Progress

To visualize the training progress of a DQN agent on CartPole, we can plot the cumulative reward over time:

```python
import matplotlib.pyplot as plt

def plot_rewards(rewards):
    plt.figure(figsize=(10, 5))
    plts.plot(np.cumsum(rewards), label='Cumulative Reward')
    plt.xlabel('Episodes')
    plt.ylabel('Cumulative Reward')
    plt.legend()
    plt.show()
```

## Asynchronous Advantage Actor-Critic (A3C)

Introduced by Ito et al. in their 2016 paper "Asynchronous Methods for Deep Reinforcement Learning," A3C is an actor-critic algorithm that uses multiple actors to explore the environment asynchronously, leading to faster convergence and better performance.

### Implementation

A3C involves two main components: the actor and the critic. The actor updates a policy network while the critic evaluates it using value networks. Here's an example of implementing A3C in Python:

```python
import threading
from keras.models import Model
from keras.layers import Input, Dense
from collections import deque
import numpy as np

class ActorCriticNetwork(object):
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        # Initialize the actor and critic models
        self.actor_model = self._build_actor_model()
        self.critic_models = [self._build_critic_model(), self._build_critic_model()]

    def _build_actor_model(self):
        state_input = Input(shape=(self.state_size,))
        layer1 = Dense(24)(state_input)
        layer2 = Dense(24)(layer1)
        out_actions = Dense(self.action_size, activation='softmax')(layer2)
        model = Model(inputs=state_input, outputs=out_actions)
        return model

    def _build_critic_model(self):
        state_input = Input(shape=(self.state_size,))
        layer1 = Dense(24)(state_input)
        layer2 = Dense(24)(layer1)
        out_value = Dense(1, activation='linear')(layer2)
        model = Model(inputs=state_input, outputs=out_value)
        return model
```

### Visualizing A3C Training Progress

To visualize the training progress of an A3C agent on a given environment, we can plot the average reward per episode:

```python
def plot_rewards(average_rewards):
    plt.figure(figsize=(10, 5))
    plts.plot(np.cumsum(average_rewards)/np.arange(len(average_rewards)+1), label='Average Reward')
    plt.xlabel('Episodes')
    plt.ylabel('Average Reward')
    plt.legend()
    plt.show()
```

## Proximal Policy Optimization (PPO)

Proximal Policy Optimization is an on-policy algorithm that uses a trust region to update the policy network, leading to stable and efficient learning. PPO was introduced by Schulman et al. in their 2015 paper "Continuous Control with Deep Reinforcement Learning."

### Implementation

PPO involves two main components: the policy model (actor) and value function models (critic). The algorithm maintains a trust region to ensure small updates to the policy network. Here's an example of implementing PPO in Python using Keras-RL library:

```python
from keras_policy import PolicyNetwork
from keras_policy import ValueNetwork
from rl.agents.ppo import PPOLearningAgent

# Create a custom policy network and value function networks
def create_networks(state_size, action_size):
    actor = PolicyNetwork([state_size], [action_size])
    critic1 = ValueNetwork([state_size], [1])
    critic2 = ValueNetwork([state_size], [1])

    return actor, critic1, critic2

# Create a PPO agent using Keras-RL library
def create_agent(actor, critic1, critic2):
    ppo_agent = PPOLearningAgent({
        'network': [actor, critic1, critic2],
        # ... (Add more parameters and hyperparameters)
    })
    return ppo_agent
```

### Visualizing PPO Training Progress

To visualize the training progress of a PPO agent on CartPole or another environment, we can plot the average reward per episode:

```python
def plot_rewards(average_rewards):
    plt.figure(figsize=(10, 5))
    plts.plot(np.cumsum(average_rewards)/np.arange(len(average_rewards)+1), label='Average Reward')
    plt.xlabel('Episodes')
    plt.ylabel('Average Reward')
    plt.legend()
    plt.show()
```

## Conclusion

Deep reinforcement learning has revolutionized the field of AI training, and DQN, A3C, and PPO are three widely used algorithms that have shown remarkable results in various domains. By understanding their concepts, implementations, and visualizing their progress through plots, we can better grasp how they work and apply them to our own projects.