1595851920
Getting Started With Reinforcement Learning(MuJoCo and OpenAI Gym)
Informal Introduction
Recall your childhood, when your curiosity was at its peak! Your inquisitiveness would lead you to explore the lands where no child has gone before. Often, these expeditions ended up in misadventures, when your wrongdoings were caught and the consequences, unfortunate; like, not being allowed to watch your favourite cartoon or to undertake the noble responsibility of saving Mario’s princess!
Those punishments were not to curb your free-thinking but to set a ‘precedent’ as to what is wrong and that ‘action’ should not be repeated.
Growing up, in younger classes, I didn’t care much for why I was taught about the American Revolution or Shakespeare’s work. But what I did care about was that I if I get good grades, I would be getting a new video game for my PC. The result? I always remained in the top few of my class throughout my school life. (Obviously, in hope for new video games)
To summarize, I was more likely to repeat my ‘action’ if I got a ‘reward’ for that.
The above example might be the most simple though certainly not the most accurate way to represent how the ‘reinforcement learning’ works.
An example of Reinforcement Learning in the real world.
Formal Introduction
An ‘agent’ has a set of ‘action space’ which it can perform in a given ‘environment’ for which it gets ‘rewarded’ if that action meets some criteria. The agent ought to take actions to maximize this reward.
This is the basic principle of reinforcement learning.
Reinforcement Learning is a specialized field of artificial intelligence which has many applications in the field of Robotics, Industrial Automation, Business Applications etc.
Some commonly used terms in reinforcement learning
- **Action Space: **The set of all the possible unique action an agent can take in any environment. For example, a mountain car can move up or down.
- **Agent: **The entity that performs an action.
- **Observation Space: **An environment-specific object representing your observation of the environment. For example, the velocity of the mountain car, the coordinates of a robotic arm in space.
- **Reward: **A value used to evaluate the impactfulness of the last action performed.
- Policy: The plan or the strategy for the next action to be performed.
- **Episode: **All the states between the initial and final states constitute an episode.
Recent Advancements
Be it IBM’s Deep Blue v/s Kasparov, AlpaGo v/s Lee Sedol or Google’s Agile and Intelligent Locomotion, reinforcement learning has made an impressive mark in proving its capability in performing intelligent tasks in a complex environment.
Agile and Intelligent Locomotion via Deep Reinforcement Learning
With the current COVID-19 situation, reinforcement learning can be an excellent tool for use in robotics and medical field for performing remote non-contact surgeries and disinfecting surfaces.
Let’s move on to setting up the system for working with MuJoCo and OpenAI Gym.
Reinforcement Learning with OpenAI Gym
OpenAI Gym is a great open-source tool for working with reinforcement learning algorithms. Before Gym existed, researchers faced the problem of unavailability of standard environments which they could use for development rapid prototyping of their algorithms.
With the advent of the Gym, it made reinforcement learning a more practical and implementable advancement/alternative to traditional machine learning methods.
Gym: A toolkit for developing and comparing re
#robotics #automation #openai #reinforcement-learning #artificial-intelligence #deep learning
What is GEEK
Buddha Community
1595851920
Getting Started With Reinforcement Learning(MuJoCo and OpenAI Gym)
Informal Introduction
Recall your childhood, when your curiosity was at its peak! Your inquisitiveness would lead you to explore the lands where no child has gone before. Often, these expeditions ended up in misadventures, when your wrongdoings were caught and the consequences, unfortunate; like, not being allowed to watch your favourite cartoon or to undertake the noble responsibility of saving Mario’s princess!
Those punishments were not to curb your free-thinking but to set a ‘precedent’ as to what is wrong and that ‘action’ should not be repeated.
Growing up, in younger classes, I didn’t care much for why I was taught about the American Revolution or Shakespeare’s work. But what I did care about was that I if I get good grades, I would be getting a new video game for my PC. The result? I always remained in the top few of my class throughout my school life. (Obviously, in hope for new video games)
To summarize, I was more likely to repeat my ‘action’ if I got a ‘reward’ for that.
The above example might be the most simple though certainly not the most accurate way to represent how the ‘reinforcement learning’ works.
An example of Reinforcement Learning in the real world.
Formal Introduction
An ‘agent’ has a set of ‘action space’ which it can perform in a given ‘environment’ for which it gets ‘rewarded’ if that action meets some criteria. The agent ought to take actions to maximize this reward.
This is the basic principle of reinforcement learning.
Reinforcement Learning is a specialized field of artificial intelligence which has many applications in the field of Robotics, Industrial Automation, Business Applications etc.
Some commonly used terms in reinforcement learning
- **Action Space: **The set of all the possible unique action an agent can take in any environment. For example, a mountain car can move up or down.
- **Agent: **The entity that performs an action.
- **Observation Space: **An environment-specific object representing your observation of the environment. For example, the velocity of the mountain car, the coordinates of a robotic arm in space.
- **Reward: **A value used to evaluate the impactfulness of the last action performed.
- Policy: The plan or the strategy for the next action to be performed.
- **Episode: **All the states between the initial and final states constitute an episode.
Recent Advancements
Be it IBM’s Deep Blue v/s Kasparov, AlpaGo v/s Lee Sedol or Google’s Agile and Intelligent Locomotion, reinforcement learning has made an impressive mark in proving its capability in performing intelligent tasks in a complex environment.
Agile and Intelligent Locomotion via Deep Reinforcement Learning
With the current COVID-19 situation, reinforcement learning can be an excellent tool for use in robotics and medical field for performing remote non-contact surgeries and disinfecting surfaces.
Let’s move on to setting up the system for working with MuJoCo and OpenAI Gym.
Reinforcement Learning with OpenAI Gym
OpenAI Gym is a great open-source tool for working with reinforcement learning algorithms. Before Gym existed, researchers faced the problem of unavailability of standard environments which they could use for development rapid prototyping of their algorithms.
With the advent of the Gym, it made reinforcement learning a more practical and implementable advancement/alternative to traditional machine learning methods.
Gym: A toolkit for developing and comparing re
#robotics #automation #openai #reinforcement-learning #artificial-intelligence #deep learning
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
1591449013
Reinforcement Learning with OpenAI GYM
Reinforcement Learning with OpenAI GYM
We are going exploring some of the deep learning puzzles on openAI gym as well as caught up an agent to play them . openAI gym is pretty much like a gym for testing our different reinforcement learning algorithms on various simulated environments with the overall goal of maximizing the reward from interacting with that environment.so lets see what is Reinforcement Learning
#reinforcement-learning #artificial-intelligence #machine-learning #openai gym
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
1621386420
Getting Started With Reinforcement Learning
Demystifying some of the main concepts and terminologies associated with Reinforcement Learning and their association with other fields of AI
Introduction
Today, Artificial Intelligence (AI) has undergone impressive advancements. AI can be subdivided into three different levels according to the ability of machines to perform intellectual tasks logically and independently:
- Narrow AI: machines are more efficient than humans in performing very specific tasks (but not trying to perform other types of tasks).
- General AI: machines are as intelligent as human beings.
- Strong AI: machines perform better than humans in different ambit (in tasks that we might or not be able to perform at all).
Right now, thanks to Machine Learning, we have been able to achieve good competency at the Narrow AI level. There are three main types of machine learning algorithms used:
- Supervised Learning: using a labelled training set to train a model, to then make predictions on unlabelled data.
- **_Unsupervised Learning: _**giving a model an unlabelled data-set, the model has then to try to find patterns in the data to make predictions.
- Reinforcement Learning: training a model trough a reward mechanism to encourage positive behaviours in case of good performance (particularly used in agent-based simulations, gaming and robotics).
Reinforcement Learning, is now considered to be the most promising technique in order to move to the next level in the AI paradigm
Reinforcement Learning (RL)
One of the reasons why Reinforcement Learning has gained so much interest today, is its interdisciplinarity. The core concepts of this area, follow in fact basic game theory, evolutionary and neuroscience principles.
Compared to all the other forms of Machine Learning, RL can, in fact, be considered to be the closest approximation in trying to replicate how humans and animals learn throughout time.
Reinforcement Learning advocates that the main way which humans most commonly use in order to learn is by using their sensors and interacting with an environment (therefore without necessarily external guidance, like in supervised learning, but by a trial and error process).
On a daily basis, we try to accomplish new tasks and depending on the results of our attempts we affect the environment around us. By assessing our attempts we can then learn through experience to identify which actions gave us greater benefits (and therefore are most convenient to repeat) and which ones should instead be best to avoid. This iterative process is summarized in Figure 2 and represents the main workflow of most Reinforcement Learning based algorithms.
An agent (eg. software bot, robot) is placed in an environment and by interacting with it can learn, receive new stimulus and create new states (eg. unlock a new scenarios or modify the structure of the exstisting ones). Every action of our agent is then associated with a reward value assessing its efficacy towards achieving a predefined goal.
#ai & machine learning #started #reinforcement learning