Chelsie  Towne

Chelsie Towne

1598770200

Reinforcement Learning for Beginners: Q-learning and SARSA

Reinforcement learning is a fast-moving field. Many companies are realizing the potential of RL. Recently, Google’s DeepMind success in training RL agent AlphaGo to defeat the world Go Champion is astounding.

#ai #reinforcement-learning #machine-learning #sarsa #q-learning

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Reinforcement Learning for Beginners: Q-learning and SARSA
Annalise  Hyatt

Annalise Hyatt

1598647500

Reinforcement Learning for Beginners: Q-learning and SARSA

Reinforcement learning is a fast-moving field. Many companies are realizing the potential of RL. Recently, Google’s DeepMind success in training RL agent AlphaGo to defeat the world Go Champion is just astounding.

But what is RL? RL is a branch of machine learning where the agent learns a behavior by trial and error. That means the agent interacts with its environment without any explicit supervision, the “desired” behavior is emphasized by a feedback signal called a reward. The agent is rewarded when taking a “good” action or it can be “punished” when it takes a “bad” action.

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In RL terminology, observations are known as states. Hence, the agent learning path comprises a series of actions taken on states and getting rewards as feedback. At the early stages of learning, the agent doesn’t know the best action to take in a specific state, after all that is the whole learning objective.

The agent objective is to maximize the sum of the rewards in a long-term. The maximization is long-term meaning that we are not only concerned with taking actions that yield the highest immediate reward but more generally, the agent is trying to learn the best strategy that gives best cumulative reward in a long term. Some of the rewards can be delayed. This objective is described as maximizing the expected return, written in math as follows:

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where R is the immediate reward and γ is known as discount factor.

When γ is closer to 0, the agent is near-sighted (gives more emphasis on the immediate reward). If the discount factor is closer to 1, the agent is more far-sighted.

The goal of RL algorithms is to estimate the expected return when the agent takes an action in a given state while following a policy. These are known as Q-values and estimate “how good” it is for the agent to take a given action in a a given state.

Q-learning is one of the most popular RL algorithms. QL allows the agent to learn the values of state-action pairs through continuous updates. As long as each state-action pair are visited and updated infinitely often, QL guarantee an optimal policy. The equation for updating the values of state-action pairs in QL is given as:

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#ai #reinforcement-learning #machine-learning #sarsa #q-learning

Chelsie  Towne

Chelsie Towne

1598770200

Reinforcement Learning for Beginners: Q-learning and SARSA

Reinforcement learning is a fast-moving field. Many companies are realizing the potential of RL. Recently, Google’s DeepMind success in training RL agent AlphaGo to defeat the world Go Champion is astounding.

#ai #reinforcement-learning #machine-learning #sarsa #q-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

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

Tia  Gottlieb

Tia Gottlieb

1595573880

Deep Reinforcement Learning for Video Games Made Easy

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 best RL learning environment available today. Additionally, we even got the library to work on Windows, which we think is 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.

1. Brief Introduction to Reinforcement Learning and Deep Q-Learning

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

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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.

2. Installation

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