1591450740

Game Level Design with Reinforcement Learning

Overview of the “PCGRL” paper presenting a novel approach to procedurally generate game levels by training RL agents.

Procedural Content Generation (or PCG) is a method of using a computer algorithm to generate…

#data-science #game-development #reinforcement-learning #machine-learning

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In this post, we will investigate how easily we can train a Deep Q-Network (DQN) agent (Mnih et al., 2015) for Atari 2600 games using the Google reinforcement learning library Dopamine. While many RL libraries exist, this library is specifically designed with **four essential features** in mind:

- Easy experimentation
- Flexible development
- Compact and reliable
- Reproducible

_We believe these principles makes _

_Dopamine _one of the. Additionally, we even got the library to work on Windows, which we think isbest RL learning environment available today!quite a feat

In my view, the visualization of any trained RL agent is an **absolute must** in reinforcement learning! Therefore, we will (of course) include this for our own trained agent at the very end!

We will go through all the pieces of code required (which is** minimal compared to other libraries**), but you can also find all scripts needed in the following Github repo.

The general premise of deep reinforcement learning is to

“derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations.”

- Mnih et al. (2015)

As stated earlier, we will implement the *DQN model* by *Deepmind*, which only uses raw pixels and game score as input. The raw pixels are processed using convolutional neural networks similar to image classification. The primary difference lies in the **objective function**, which for the DQN agent is called the *optimal action-value function*

where_ rₜ *is the maximum sum of rewards at time t discounted by γ, obtained using a behavior policy* π = P(a_∣_s)_ for each observation-action pair.

There are relatively many details to Deep Q-Learning, such as *Experience Replay* (Lin, 1993) and an _iterative update rule. _Thus, we refer the reader to the original paper for an excellent walk-through of the mathematical details.

One key benefit of DQN compared to previous approaches at the time (2015) was the ability to outperform existing methods for Atari 2600 games using the **same set of hyperparameters** and **only pixel values and game score as input**, clearly a tremendous achievement.

This post does not include instructions for installing Tensorflow, but we do want to stress that you can use **both the CPU and GPU versions**.

Nevertheless, assuming you are using `Python 3.7.x`

, these are the libraries you need to install (which can all be installed via `pip`

):

```
tensorflow-gpu=1.15 (or tensorflow==1.15 for CPU version)
cmake
dopamine-rl
atari-py
matplotlib
pygame
seaborn
pandas
```

#reinforcement-learning #q-learning #games #machine-learning #deep-learning #deep learning

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The Association of Data Scientists (AdaSci), a global professional body of data science and ML practitioners, is holding a full-day workshop on building games using reinforcement learning on Saturday, February 20.

Artificial intelligence systems are outperforming humans at many tasks, starting from driving cars, recognising images and objects, generating voices to imitating art, predicting weather, playing chess etc. AlphaGo, DOTA2, StarCraft II etc are a study in reinforcement learning.

Reinforcement learning enables the agent to learn and perform a task under uncertainty in a complex environment. The machine learning paradigm is currently applied to various fields like robotics, pattern recognition, personalised medical treatment, drug discovery, speech recognition, and more.

With an increase in the exciting applications of reinforcement learning across the industries, the demand for RL experts has soared. Taking the cue, the Association of Data Scientists, in collaboration with Analytics India Magazine, is bringing an extensive workshop on reinforcement learning aimed at developers and machine learning practitioners.

#ai workshops #deep reinforcement learning workshop #future of deep reinforcement learning #reinforcement learning #workshop on a saturday #workshop on deep reinforcement learning

1591450740

Game Level Design with Reinforcement Learning

Overview of the “PCGRL” paper presenting a novel approach to procedurally generate game levels by training RL agents.

Procedural Content Generation (or PCG) is a method of using a computer algorithm to generate…

#data-science #game-development #reinforcement-learning #machine-learning

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Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

- Temporal difference learning (TD learning)
- Parameters
- QL & SARSA
- Comparison
- Implementation
- Conclusion

We will compare these two algorithms via the CartPole game implementation. **This post’s code can be found** **here** **:****QL code** **,****SARSA code** **, and** **the fully functioning code** **.** (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

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Although the field of deep learning is evolving extremely fast, unique research with the potential to get us closer to Artificial General Intelligence (AGI) is rare and hard to find. One exception to this rule can be found in the field of meta-learning. Recently, meta-learning has also been applied to Reinforcement Learning (RL) with some success. The paper “Discovering Reinforcement Learning Agents” by Oh et al. from DeepMind provides a new and refreshing look at the application of meta-learning to RL.

**Traditionally, RL relied on hand-crafted algorithms **such as Temporal Difference learning (TD-learning) and Monte Carlo learning, various Policy Gradient methods, or combinations thereof such as Actor-Critic models. These RL algorithms are usually finely adjusted to train models for a very specific task such as playing Go or Dota. One reason for this is that multiple hyperparameters such as the discount factor γ and the bootstrapping parameter λ need to be tuned for stable training. Furthermore, the very update rules as well as the choice of predictors such as value functions need to be chosen diligently to ensure good performance of the model. The entire process has to be performed manually and is often tedious and time-consuming.

DeepMind is trying to change this with their latest publication. In the paper, the authors propose a **new meta-learning approach that discovers the learning objective as well as the exploration procedure by interacting with a set of simple environments**. They call the approach the **Learned Policy Gradient (LPG)**. The most appealing result of the paper is that the algorithm is able to effectively generalize to more complex environments, suggesting the potential to discover novel RL frameworks purely by interaction.

In this post, I will try to explain the paper in detail and provide additional explanation where I had problems with understanding. Hereby, I will stay close to the structure of the paper in order to allow you to find the relevant parts in the original text if you want to get additional details. Let’s dive in!

#meta-learning #reinforcement-learning #machine-learning #ai #deep-learning #deep learning