What is reinforcement learning?
Reinforcement learning is one subfield of machine learning. It involves acting appropriately to maximize reward in a particular circumstance. It is used by various programs and machines to determine the optimal course of action to pursue in a given case. Reinforcement learning has no right or wrong solution; instead, the reinforcement agent decides what to do to finish the task. This differs from supervised learning, where the training data includes the solution key, and the model is trained with that answer. It is obligated to gain knowledge from its experience without a training dataset.
Reinforcement learning’s main points
- Input: The input should represent the starting point for the model.
- Output: There are as many possible outputs as there are different ways to solve a particular issue.
- Training: The training is based on input. The user will choose whether to reward or penalize the model based on its returns state.
- The model never stops learning.
- The best course of action is selected based on the most significant benefit.
There are two distinct categories of reinforcement:
Positive reinforcement is when an event that results from a particular behavior strengthens and becomes more frequent. In other words, it influences behavior in a good way.
The following benefits of reinforcement learning:
- Boosts Performance
- Maintain Change for a Protracted Period
- The results may be weakened by an excess of states brought on by excessive reinforcement.
Negative reinforcement strengthens a behavior by stopping or avoiding a negative condition.
Reinforcement learning benefits include:
- Enhances Behavior
- Show disdain for a required level of performance
- It only offers sufficient resources to meet the bare minimum of behavior.
Top reinforcement learning tools/platforms/libraries
The most well-liked platform for creating and comparing reinforcement learning models, OpenAI Gym, is fully compatible with powerful computing libraries like TensorFlow. The Python-based rich AI simulation environment supports training agents using traditional video games like Atari and other scientific disciplines like robotics and physics using tools like Gazebo and MuJoCo simulators.
Additionally, the gym environment provides APIs for feeding observations and rewarding agents. A new platform called Gym Retro, created by OpenAI has just been made available. It has 58 distinct and different scenarios from the Sonic the Hedgehog, Sonic the Hedgehog 2, and Sonic 3 video games. Developers of AI games and reinforcement learning aficionados can sign up for this challenge.
More than 95,000 developers use this well-known open-source library from Google every day in fields including robotics, intelligent chatbots, and natural language processing. TensorLayer, an extension of TensorFlow created by the community, offers well-liked RL modules that are simple to adapt and put together to solve practical machine learning problems.
With fewer lines of code and faster execution, Keras makes neural network implementation simple. It centers on the model architecture and offers senior developers and principal scientists a high-level interface to the TensorFlow high-tensor computation framework. Therefore, if you already have any RL models created in TensorFlow, just choose the Keras framework and apply your learning to the relevant machine learning challenge.
A Google 3D platform with customization for agent-based AI research is called DeepMind Lab. It is used to comprehend how autonomous artificial agents pick up complex skills in vast, unobserved environments. DeepMind gained popularity after its AlphaGo program beat human goes players at the beginning of 2016. The DeepMind team is concentrating on core AI foundations, including developing a single AI system supported by cutting-edge techniques and distributional reinforcement learning, from its three centers in London, Canada, and France.
Another well-known deep learning library used by many reinforcement learning researchers is Pytorch, which Facebook made publicly available. In a recent Kaggle competition, the top 10 finishers virtually universally favored it. RL practitioners use it wisely to do experiments on creating policy-based agents and to develop new adventures since it has dynamic neural networks and powerful GPU acceleration. Playing GridWorld is one insane research project in which Pytorch unlocked its potential using well-known RL techniques like policy gradient and the streamlined Actor-Critic method.
Dopamine is to reinforcement learning what cheat codes are to video games. Dopamine is, in essence, a shortcut for real-life practice. It is designed to assist researchers in presenting speedy results when using RL. It is based on Tensorflow, although it is not a Google product.
Dopamine strives to be adaptable, dependable, and repeatable. The first iteration focuses on supporting the cutting-edge, single-GPU Rainbow agent used for playing Atari 2600 games (Hessel et al., 2018). (Bellemare et al., 2013). A complicated setup and a series of processes are required to code RL. With the aid of dopamine, you may ease into this.
Reagent, formerly known as Horizon, tries to train RL models in a batch context. The framework is entirely based on PyTorch, much like it is by Facebook. Data preparation is the first step in the workflow that the framework assists with. Real-time deployment, not fast experimentation, is the goal of Reagent.
The official literature lists six main algorithms you can work on, but with a bit of imagination, there is room for significant growth. The framework concentrates on the complete workflow, and employing it may actually get good outcomes. The main issue is that there is no pip installer, which challenges using this framework. The official paper and the source code are available here.
Huskarl is based on TensorFlow and Keras and means “warrior” in Old Norse. To the list of open-access RL frameworks, it is a recent addition. Huskarl promises to be modular and quick to prototype. Huskarl, which is extremely computationally intensive, makes it simple to use many CPU cores for parallel computing. One of the leading causes for its quick prototyping is this.
Huskarl is compatible with Unity3d for multi-agent environments and Open AI gym, which we shall describe shortly. Now, only a few algorithms can be used, but more are on the way.
One of the most frequent contributors to open-source deep learning stacks is DeepMind. Even in 2019, Alphabet’s DeepMind unveiled OpenSpiel, a reinforcement learning framework with a gaming focus. The framework consists of a collection of environments and algorithms that can support research on general reinforcement learning, mainly when applied to gaming. In addition to tools for browsing and planning in games, OpenSpiel also offers tools for studying learning dynamics and other widely used evaluation metrics.
The framework supports more than 20 different single- and multi-agent game types, such as sequential, cooperative, zero-sum, and one-shot games. That is in addition to games with tight turn-taking requirements, auction games, matrix games, and simultaneous-move games, as well as perfect games (where participants have excellent knowledge of all the events that have already happened when making a decision) and imperfect information games (where decisions are made simultaneously).
The TF-Agents framework for TensorFlow was created as an open-source infrastructure paradigm to support the development of parallel RL algorithms. To make it simple for users to develop and apply algorithms, the framework offers a variety of components that correspond to the critical elements of an RL problem.
The framework’s environments are all created using unique Python processes. The platform mimics two simultaneous environments instead of performing solitary observations and instead runs the neural network computation on a batch. As a result, the TensorFlow engine can now parallelize calculations without human synchronization.
The need for computing resources has increased along with the number of machine learning projects. Uber AI introduced Fiber, a Python-based library that functions with computer clusters, to help solve this problem. The initial goal of Fiber’s development was to support large-scale parallel computing initiatives within Uber.
Like ipyparallel, spark, and the standard Python multiprocessing module, Fiber is iPython for parallel computing. The fiber was broken down into three layers: the cluster layer, the backend layer, and the API layer, to run on various cluster management systems. According to Uber AI’s research, Fiber performed better than its competitors for shorter jobs.
Fiber is skilled at handling errors in pools. A new pool’s associated task queue, result queue, and pending table are all established simultaneously. Each new task is put into the line and distributed across the worker and master processes. A user selects a job from the queue and executes its functions. An entry is added to the pending table once a task from the task queue has been completed.
The Python library Pyqlearning is used to implement RL. It emphasizes multi-agent Deep Q-Network and Q-Learning. Pyqlearning offers design elements rather than cutting-edge “black boxes” for end users. It can create information search algorithms, such as web crawlers or GameAI. As a result, using this library is challenging.
A Python reinforcement learning framework with numerous cutting-edge algorithms is called Reinforcement Learning Coach (Coach) by Intel AI Lab.
It exposes a collection of simple-to-use APIs for testing out new RL algorithms. The library’s parts are modular, including the algorithms, environments, and neural network designs. Thus, it is relatively simple to extend and reuse existing components.
With MushroomRL, you may use popular Python libraries for tensor computing and RL benchmarks, thanks to the library’s modular design.
It provides deep RL algorithms and standard RL techniques to enable RL experimentation. The concept of MushroomRL is to provide a standard interface via which most RL algorithms can be executed with minimal effort.
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Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications