Angela  Dickens

Angela Dickens

1598466660

Reinforcing the Science Behind Reinforcement Learning

You’re getting bore stuck in lockdown, you decided to play computer games to pass your time.

You launched Chess and chose to play against the computer, and you lost!

But how did that happen? How can you lose against a machine that came into existence like 50 years ago?

Image for post

This is the magic of** Reinforcement learning.**

**Reinforcement learning lies under the umbrella of Machine Learning. **They aim at developing intelligent behavior in a complex dynamic environment. Nowadays since the range of AI is expanding enormously, we can easily locate their importance around us. From _Autonomous Driving, Recommender Search Engines, Computer games to Robot skills, _AI is playing a vital role.

Pavlov’s Conditioning

When we think about AI, we have a perception of thinking about the future, but our idea takes us back in the late 19th century, Ivan Pavlov, a Russian physiologist was studying the salivation effect in dogs. He was interested in knowing how much dogs salivate when they see food, but, while conducting the experiment, he noticed that dogs were even salivating before seeing any food. After his conclusions on that experiment, Pavlov would ring a bell before feeding them and as expected they again started salivating. The reason behind their behavior can be their ability to learn** because they had learned that after the bell, they’ll be fed**. Another thing to ponder is, the dog doesn’t salivate because the bell is ringing but because given past experiences he had learned that food will follow the bell.

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

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Reinforcing the Science Behind Reinforcement Learning
Angela  Dickens

Angela Dickens

1598466660

Reinforcing the Science Behind Reinforcement Learning

You’re getting bore stuck in lockdown, you decided to play computer games to pass your time.

You launched Chess and chose to play against the computer, and you lost!

But how did that happen? How can you lose against a machine that came into existence like 50 years ago?

Image for post

This is the magic of** Reinforcement learning.**

**Reinforcement learning lies under the umbrella of Machine Learning. **They aim at developing intelligent behavior in a complex dynamic environment. Nowadays since the range of AI is expanding enormously, we can easily locate their importance around us. From _Autonomous Driving, Recommender Search Engines, Computer games to Robot skills, _AI is playing a vital role.

Pavlov’s Conditioning

When we think about AI, we have a perception of thinking about the future, but our idea takes us back in the late 19th century, Ivan Pavlov, a Russian physiologist was studying the salivation effect in dogs. He was interested in knowing how much dogs salivate when they see food, but, while conducting the experiment, he noticed that dogs were even salivating before seeing any food. After his conclusions on that experiment, Pavlov would ring a bell before feeding them and as expected they again started salivating. The reason behind their behavior can be their ability to learn** because they had learned that after the bell, they’ll be fed**. Another thing to ponder is, the dog doesn’t salivate because the bell is ringing but because given past experiences he had learned that food will follow the bell.

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

Larry  Kessler

Larry Kessler

1617355640

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

Crystal Clear Reinforcement Learning

Reinforcement learning (RL) is the hottest field of Artificial Intelligence and Machine Learning with many breathtaking breakthroughs in the last couple of years. This article is an attempt to give a concise view of the entire RL spectrum without going too much into math and formula while not losing sight of trees in the dense & complex forest.

Now first, let’s understand what is Reinforcement Learning or RL?

RL means taking optimal action with long term results or cumulative rewards in mind.

Pixabay images used for this illustration picture

Reinforcement learning (RL) is learning by interacting with an environment. A Reinforcement Learning agent learns from the consequences of its actions rather than from being explicitly taught, and it selects its actions based on its past experiences (exploitation) and also by new choices (exploration), which are essential, trial and error learning just like a child learns. The reinforcement signal that the RL-agent receives is a numerical reward, which encodes the success of an action’s outcome, and the agent seeks to learn to select actions that maximize the cumulative reward over time.

Pixabay images used for this illustration picture

Before we dive deep inside RL, lets first take a look at why RL has so much importance in Artificial Intelligence and Machine Learning horizon. Refer to the below diagram that shows RL is used in every sphere of life.

Pixabay images used for this illustration picture

Now, I will cover a few terms frequently used to explain Reinforcement Learning, and one must understand these before diving into an algorithm and more engaging concepts.

Pixabay images used for this illustration picture

In the above diagram, I have listed name, standard notation, and pictorial representation. Now I will define each of them.

Agent

Anything that senses its environment using some kind of sensor and able to generate action in the environment is called an agent. The agent executes the action, receives observation, and rewards.

Environment

The environment is the overall representation of which agent interacts. The agent is not considered part of the environment. The environment receives an action, emits observation, and the reward.

State

The state describes the current situation and determines what happens next. The agent may have a partial view of the state, which is called observation.

Reward

When an agent takes action in a state, it receives a reward. Here the term “reward” is an abstract concept that describes feedback from the environment. A reward can be positive or negative. When the reward is positive, it is corresponding to our usual meaning of reward. When the reward is negative, it is corresponding to what we usually call “punishment.”

Pixabay, Atari & Tesla images used for this illustration picture

The reward is the feedback the agent gets from the environment at every time step. It can then use this reward signal (can be positive for a good action or negative for a bad action) to conclude how to behave in a state. The goal, in general, is to solve a given task with the maximum reward possible. That is why many algorithms have a tiny negative reward for each action the agent takes to animate it to solve the task as fast as possible. The reward is a design decision that is critical in RL as this would encourage the RL agent to optimize the behavior. For example, winning the chess game at less time or drive the car at average speed but without any collision (car can speed up in empty high-way while drive slowly in the busy road). Reward tells what is good in an immediate sense. For example, in the Atari game, every time step agent gets points or loses points.

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

Jackson  Crist

Jackson Crist

1617331066

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

Tia  Gottlieb

Tia Gottlieb

1598250000

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