Reinforcement Learning with TensorFlow Agents 

Once, I got to know TF Agent, a library for RL based on TensorFlow and with the full support of its community (note that TF Agent is not an official Google product but it is published as an archive from the official TensorFlow account on Github).
I am currently using TF Agent in a project and it’s easy to get started with it, thanks to the good documentation that includes instructions. It is updated regularly and has a lot of contributors, which makes me think that maybe we will consider TF Agency as the standard framework for implementing RL in the near future. Because of this, Continve decided to make this article a quick introduction to you, so you can also benefit from this library. I have published all the code used here as a Google colab laptop, so you can easily run it online.

#machine-learning #reinforcement-learning #data-science #programming #artificial-intelligence

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Reinforcement Learning With Tensorflow Agents | Tensorflow Reinforcement Learning [2021]

Reinforcement learning has gained valuable popularity with the relatively recent success of DeepMind’s AlphaGo method to baeat the world champion Go player. The AlphaGo method was educated in part by reinforcement learning on deep neural networks.

This style of learning is a distinct feature of machine learning from the classical supervised and unsupervised paradigms. In reinforcement learning, the network responds to environmental data (called the state) using deep neural networks, and influences the behaviour of an agent to try to optimise a reward.

This technique helps a network to learn how to play sports, such as Atari or other video games, or some other challenge that can be rewritten as a form of game. In this tutorial, a common model of reinforcement learning, I will introduce the broad principles of Q learning, and I will demonstrate how to incorporate deep Q learning in TensorFlow.

Introduction to reinforcement learning

As mentioned above, reinforcement learning consists of a few basic entities or principles. They are: an environment that creates a condition and reward, and an entity that performs actions in the given environment. In the diagram below, you see this interaction:

The task of the agent in such a setting is to analyse the state and the incentive information it receives and pick an behaviour that maximises the input it receives from the reward. The agent learns by repetitive contact with the world, or, in other words, repeated playing of the game.

In order to succeed, it is necessary for the agent to:

1. Learn the link between states, behaviour and resulting incentives

2. Determine which is the best move to pick from (1)

Implementation (1) requires defining a certain set of principles that can be used to notify (2) and (2) is referred to as the strategy of operation. One of the most common methods of applying (1) and (2) using deep Q is the Deep Q network and the epsilon-greedy policy.

#artificial intelligence #machine learning #reinforcement learning #tensorflow

Attend The Full Day Hands-On Workshop On Reinforcement Learning

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

Paper Summary: Discovering Reinforcement Learning Agents

Introduction

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

Easy Explanation Of Relational Deep Reinforcement Learning with Real Code

Due to the memory limitations of LSTMs, most of the current Deep Learning models have used attention mechanisms. A paper titled ‘Deep reinforcement learning with relational inductive biases’ about that topic was already published by DeepMind at ICLR 2019.

Relational environment

In this paper, grid type environment is used to verify the performance of the model. However, it is confirmed that that environment takes too much time to train compared to the quality of it due to the way of moving the block. That is why I decide to use another environment that has relational features but has a simple movement way. After confirming whether the algorithm of the paper works, we can use it in the Starcraft 2 environment.

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#reinforcement-learning #relational-intelligence #tensorflow #deep-learning #relational deep reinforcement learning

Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

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