Blockchain reinforcement learning

A trusted routing scheme is very important to ensure the routing security and efficiency of wireless sensor networks WSNs. There are a lot of studies on improving the trustworthiness between routing nodes, using cryptographic systems, trust management, or centralized routing decisions, etc. However, most of the routing schemes are difficult to achieve in actual situations as it is difficult to dynamically identify the untrusted behaviors of routing nodes. Meanwhile, there is still no effective way to prevent malicious node attacks.

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Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning

This recording is the first episode of a new Chainlink Research Report series which features short presentations of exceptional working papers by blockchain scholars around the world. In this episode, Dr. She received her Ph.

Berkeley and her B. She is also an academic partner with the Chainlink Labs research team and was previously awarded a Chainlink research grant to further her work. Her research interests and publications focus on the algorithmic foundations of blockchains, distributed systems, privacy-preserving technologies, and machine learning. I think this is a really cool implementation of RL, because RL is usually unstable and hard to train, but in this case, it is looking for strategies of exploiting profits, so stability would not be an issue.

It also makes me really curious about why the Nash equilibrium for 3 players and above is for them to be honest. Could it be relying on the assumption that every player has about the same computing power? Because if so, does that apply to real life? However, we did run some preliminary experiments a while back with varying number and fraction of hash power, and saw the parties appear to converge to honest mining in those settings too.

So there is at least some empirical evidence to suggest that if the conjecture is true, it is not only true when all parties have the same hash power. It would be very interesting to see if this can be proved formally, even for e. Fanti, welcome to the forum! You mentioned that this kind of selfish mining is very hard to detect; how do most platforms deal with this type of behavior?

This is indeed the textbook intersection between Deep Reinforcement learning based Data science and Blockchain via analysis of Blockchain incentive mechanisms! I would imagine being able to give constructive feedback to blockchain protocol providers be a priceless insight to further develop end user adoption to their Blockchain protocol. To imagine analysis of Block chain mechanisms vulnerability is already underway while the adoption of the Blockchain towards Web 3. Indeed, I think the biggest hurdle for Blockchain companies is a well defined incentive mechanism to have end users go on chain.

Competing against well established cloud services and its centralized ease of use, cross platform convenience and conventional paradigm of Web 2. To this day, most industries are still trying to adapt to the Web 2. Out of the blue, I wonder how feasible is it to undergo such DRL analysis of block chain incentive mechanism?

I would imagine setting up a Blockchain Paas to market others to go on chain be a difficult task in and of itself. How would the AIops data pipeline Blockchain architecture look like before even running such analysis? Thank you for the question! Selfish mining actually is detectable in the real world by looking at the rates at which miners collect rewards, proportional to their hash power.

Despite this, we have not seen substantial evidence of selfish mining in practice, to the best of my knowledge. If you or anyone else knows of studies that have observed selfish mining in the real world, please share a link! Hi Tony, in general, DRL can be sensitive to hyperparameters and unstable, as other commenters have pointed out, which makes it potentially tricky to use in practice. It can also be computationally intensive.

Our experiments were run on a departmental cluster, and each experiment could take as long as a few days to complete. We believe this is ok from a latency perspective you only need to analyze your incentive mechanism once , but the associated computational costs could be prohibitive.

I expect these costs will come down in the coming years, but these factors could certainly be a barrier to deployment. Do you think selfish mining could happen in this setting? I know because of time limitations you had to concentrate on case studies 3 and 4 in this video. Case study 4, which combined a selfish mining attack with a voting attack, piqued my curiosity. The results slide showed that SquirRL DRL framework you created to analyze incentive mechanisms in an automated way , started to show increased voting reward fraction vs.

Bottom line, combining attacks leads to a lot of possibilities. In other words, a way to prioritize the order in which SquirRL should simulate attack combinations in order to inform the community what to defend against? Or, are adversaries moving so quickly, that the community is left to simply catch-up and defend against already known attacks, much less possible combinations of attacks? Thank you for having the ingenious insight to use DRL instead of MDP to analyze this space and for taking the time to answer questions here!

Fanti, thanks very much for a fascinating presentation. In your video exchange with Jason Anastasopoulos you note that permissionless blockchains need an incentive mechanism to make the system work, but if the mechanism is poorly designed it can bring the whole system crashing down.

Finally, what other options are open to humanity, technically speaking? Do you agree with that statement? Are there any other incentive mechanisms that researchers believe could result in a more stable economy over the long term? For this reason, my guess is that in the setting you are describing, it would be similar to 2 strategic parties and 1 honest party with just a little bit of hash power. Here we see that as the two strategic parties get closer and closer to hash power, their advantage from selfish mining vanishes.

And actually, they seem to not be stealing from each other, but from the 3rd honest party, while converging to an honest strategy only when the hash power is actually So I believe SM continues to be profitable for 2 strategic players, but our experiments suggest this may not be the case for 3 players. One possibility is to give the RL agent the option of using any known attack, and it will choose which ones to exploit.

However, this can be difficult to encode in a compact action space if there are many different action spaces for different attacks, which may affect learning stability. One broader point to consider is that many of the attacks in the figure you mentioned cannot be easily translated into a direct numeric reward that is comparable to the reward from other attacks e.

Thanks for the question. DeFi applications are just that—applications. As you pointed out, they run on some underlying blockchain with its own existing consensus mechanism. Actually, the same techniques we used in SquirRL could probably be used to learn strategies for trading in DeFi apps to maximize profit.

DeFi is of course not fair at all, and there has been a lot of work showing how strategic users can profit off other naive users Chainlink researchers are doing a lot of work to try to address this problem. So in that sense, many DeFi apps may be unfair both at the application layer and at the consensus layer. I am curious as to whether or not there are any current examples of permissionless blockchains on a mass scale that you feel are showing signs of crashing because they lack the capability to fairly incentivize all miners.

Fanti, thanks for your response. What happened to that beautiful dream? DRL could theoretically be used here, aka no need for actual human ordering of attack combinations, DRL should eventually lead to the best combo of attacks after several iterations.

Another aha! I was trying to combine apples and oranges, thanks for setting me straight. Thank you so much for this fantastic presentation! In this case, a group of selfish miners colluding against each other and against the group effectively seem to balance the system by effectively negating the nefarious activity? That is a hard question to ask in text format without getting it convoluted, so please let me know if I need to explain myself better!

Description: This recording is the first episode of a new Chainlink Research Report series which features short presentations of exceptional working papers by blockchain scholars around the world. Full Video: Take-aways: Incentive mechanisms play an essential role in permissionless blockchains.

Designing incentive-compatible mechanisms, in which expression of true preferences are utility maximizing, is challenging. Little is currently known about properties of incentive mechanisms currently operating on large-scale blockchains, making it difficult to test their behavior.

Deep reinforcement learning can identify new attack strategies and replicate known strategies such as selfish mining, helping to identify and improve upon weaknesses that were previously unclear. Giulia Fanti: Dr.

Ji, P. Daian, F. Tramer, G. Fanti, A. Sivaraman, W. Tang, S. Venkatakrishnan, G. Fanti, M. Communication cost of consensus for nodes with limited memory.

Fanti, N. Holden, Y. Peres, G. Sivaraman, S. Bojja Venkatakrishnan, K. Ruan, P. Negi, L. Yang, R. Mittal, G. Design choices for central bank digital currency: Policy and technical considerations. Allen, S. Capkun, I. Eyal, G.

Fanti, B. Ford, J. Grimmelmann, A.

Building a complex reinforcement learning crypto-trading environment in python

For many years, pharmaceutical companies have struggled to track products through the supply chain. This drawback has made it easy for counterfeiters to introduce fake drugs into the market. To counteract the problem, a new system for tracking and tracing drugs is needed. Researchers believe blockchain can provide the technological foundation for such a system, because it can track legitimate drugs and help prevent the circulation of fake ones. While such drugs may include some genuine ingredients, they can also contain toxic ingredients at the production level.

To chain or not to chain: A reinforcement learning approach for blockchain-enabled IoT monitoring applications. Naram Mhaisen, Noora Fetais, Aiman Erbad.

Practically AI - Blockchain, Realtime Decision Support & Reinforcement Learning

Blockchain and deep reinforcement learning DRL are two separate transaction systems committed to the credibility and usefulness of system functionality. There is rapid growth importance in integrating both technologies into effective and stable information exchange and research solutions. Blockchain is a revolutionary platform for future generational telecommunications networking that will set up the secured and distributed information exchange framework. In combination with DRL, blockchain could significantly improve the efficiency of mobile communications. The rapid growth of networks for the Internet of Things IoT necessitates the development of suitable and reliable infrastructure as well as a significant proportion of the information. Blockchain, a distributed and reliable ledger, is often considered a considerably beneficial means of providing scientific confidentiality and security to IoT devices. Thus, it is necessary to improve transaction performance and deal with massive data transmission situations. As a result, the work presented here explores the DRL fundamental operation of the blockchain-enabled IoT system, where transactions are simultaneously strengthened, and community-based divisibility is ensured. Throughout this paper, the authors first present the decentralised and efficient structure for communication by incorporating DRL and blockchain across wireless services that allow for the scalable and reliable allocation of information. The results show that the proposed method has shorter delays and requires less transmission power.

OpenAI has created a less toxic version of GPT-3

blockchain reinforcement learning

Agent-based models ABMs use a bottom-up approach to discover complex, aggregate-level properties. These properties emerge from individual agent behaviors and interactions within their environment. Thus, once individual behavioral rules of Bitcoin trading are formulated, an ABM can generate an emerging aggregate phenomenon, such as market price, from the possibly non-linear interactions of those rules. Unfortunately, discovering these rules can be challenging and potentially require deep insight and domain knowledge. We utilize inverse reinforcement learning IRL as a method for obtaining individual rules for an ABM directly from data.

Investing into crypto currencies became one of the most popular topics in — a case in point is how the search term "cryptocurrency" on Google skyrocketed at the end of It was an opportunity to earn a lot of money in a short period of time and nobody wanted to miss it.

Cryptocurrency Portfolio Management with Deep Reinforcement Learning

This article is part of our reviews of AI research papers , a series of posts that explore the latest findings in artificial intelligence. Since they made their first appearance in , deepfakes, an artificial intelligence technology that can swap faces in videos , have been a constant source of controversy and concern. How serious a threat are they? It depends on who you ask. Lawmakers are increasingly worried that the AI-doctored videos will unleash a new wave of fake news and soon become a serious national security concern , especially as the U.

Deep Q-Learning for Trading Cryptocurrency

Blockchain technology has been trending in recent years. This technology allows a secure way for individuals to deal directly with each other through a highly secure and decentralized system, without an intermediary. In addition to its own capabilities, machine learning can help in handling many limitations that blockchain-based systems have. The combination of these two technologies Machine Learning and Blockchain Technology can provide high-performing and useful results. In this article, we will understand blockchain technology and explore how machine learning capabilities can be integrated with a blockchain technology-based system. We will also discuss some popular applications and use cases of this integrated approach.

In the last decade, blockchain and Smart Contracts (SCs) have attracted We propose a Reinforcement Learning (RL)-based approach to achieve such a.

GPT-3 access without the wait.

They combine two potent primitives: private machine learning, which allows for training to be done on sensitive private data without revealing it, and blockchain-based incentives, which allow these systems to attract the best data and models to make them smarter. The result is open marketplaces where anyone can sell their data and keep their data private, while developers can use incentives to attract the best data for their algorithms to them. Constructing these systems is challenging and the requisite building blocks are still being created, but simple initial versions look like they are starting to become possible.

Convergence of Blockchain, IoT, and AI

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However, the issue of BFL is that the training latency may increase due to the blockchain mining process. The other issue is that mobile devices in BFL have energy and CPU constraints that may reduce the system lifetime and training efficiency. To address these issues, the Machine Learning Model Owner MLMO needs to i decide how much data and energy that the mobile devices use for the training and ii determine the mining difficulty to minimize the training latency and energy consumption while achieving the target model accuracy. Nguyen Quang Hieu. Tran The Anh. Nguyen Cong Luong.

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Reinforcement Learning and Blockchain to secure the Internet of Things

Jesus Rodriguez. GPT-3 — which can answer questions, perform language analysis and generate text — might be the most famous achievements in recent years of the deep learning space. But, by no means, is it the most applicable to the crypto space. In this article, I would like to discuss some novel areas of deep learning that can have a near immediate impact in the quant models applied to crypto. In the last year, there have been active research efforts in quantitative finance exploring how transformer models can be applied to different asset classes. However, the results of these efforts remain sketchy showing that transformers are far from ready to operate in financial datasets and they remain mostly applicable to textual data.

Security and Privacy Lab

Deep RL has emerged as an important family of techniques for training autonomous agents and has led to the achievement of human-level performance on complex games such as Atari, Go, and Starcraft. However, at the same time, deep RL is also vulnerable to adversarial examples and can overfit to the training environment. In this talk, I will talk about our recent work in adversarial examples in deep RL and a framework for investigating generalization in deep RL towards the goal of building deep RL with greater resilience and generalization. Her research interest lies in deep learning, security, and blockchain.

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