1. Introduction

In this tutorial, we aim to delve deeper into the world of Reinforcement Learning (RL), exploring advanced concepts and techniques that can help in the creation of more efficient and sophisticated RL agents.

By the end of this tutorial, you will be familiar with advanced concepts like Policy Gradients, Deep Q-Networks (DQN), and Advantage Actor-Critic methods (A2C/A3C). We will also discuss about exploration vs exploitation trade-off, and various methods to handle it.

Prerequisite knowledge:
- Basic understanding of Reinforcement Learning concepts (Q-Learning, SARSA)
- Python programming
- Familiarity with machine learning libraries like TensorFlow or PyTorch

2. Step-by-Step Guide

Policy Gradients

Policy gradients methods optimize the policy directly. In these methods, we define the policy π(a|s, θ) parameterized by θ, and then we make the agent learn the optimal parameters by applying gradient ascent on the expected return.

# Implementing policy gradient in a simple example
class PolicyGradient:
    def __init__(self, num_actions, num_features):
        self.num_actions = num_actions
        self.num_features = num_features
        self.discount_factor = 0.99
        self.learning_rate = 0.01

Deep Q-Networks (DQN)

DQN is a method that uses a deep learning model as a function approximator to estimate the Q-values. It was the first technique that successfully combined reinforcement learning with deep learning.

# DQN in a nutshell
class DQN:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size

Advantage Actor-Critic methods (A2C/A3C)

A2C/A3C methods are a combination of value-based and policy-based methods. The actor updates the policy, and the critic evaluates the policy by estimating the value function.

# Implementing A3C
class A3C:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size

3. Code Examples

Each section above includes a small code snippet showing the basis of implementing the advanced methods.

4. Summary

We've covered advanced reinforcement learning concepts like Policy Gradients, Deep Q-Networks (DQN), and Advantage Actor-Critic methods (A2C/A3C). We've also seen code snippets to understand how these concepts can be implemented.

For further learning, you can look into other advanced topics like Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO).

5. Practice Exercises

1. Implement a simple Policy Gradient on the CartPole environment from OpenAI's gym.
2. Implement a Deep Q-Network on the MountainCar environment from OpenAI's gym.
3. Combine the two above and implement an Advantage Actor-Critic method on any environment of your choice.

Remember, the key to mastering reinforcement learning is practice and experimentation. Don't hesitate to modify the algorithms, play with the parameters, and see how the performance evolves. Happy Learning!