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4 results on '"OpenAIGym"'

1. ROBB: Recurrent Proximal Policy Optimization Reinforcement Learning for Optimal Block Formation in Bitcoin Blockchain Network

Author

Amit Dutta, Nafiz Imtiaz Rafin, M. Ali Akber Dewan, and Md. Golam Rabiul Alam

Subjects
Dynamic block size, reinforcement learning, blockchain, OpenAIGym, proximal policy optimization, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Blockchain is a ground-breaking technology that has changed how we manage and store protected data. It is a decentralized ledger that enables safe, open, and unchangeable record-keeping. It relies on a distributed network of nodes rather than a single central authority to check and verify transactions, guaranteeing that each entry is correct and unchangeable. Transactions in a blockchain network are grouped into blocks, which are then linked together in a chronological and immutable chain. Block size is a critical parameter in blockchain technology, which refers to the maximum size of each block in the chain that is not benchmarked yet. However, we cannot just change the block size of the blockchain. It is challenging and will create security issues. The Block size is crucial because it affects the number of transactions processed per second, the confirmation time, and overall network efficiency. The confirmation time should be faster to ensure stable earnings for the miners. Moreover, it needs help with broader applications due to high transaction fees and long verification times. We have proposed a reinforcement learning model named ROBB that can efficiently create a block considering the current network state and previous transactions. At first, the problem was converted into a reinforcement learning environment to solve using multiple reinforcement algorithms. We developed a blockchain simulator to replicate the network environment. To transform it into a reinforcement learning environment, we integrated it with OpenAI Gym. The simulator was trained by generating random transactions. Finally, we designed a reward function that enables the simulator to hold transactions and create blocks with the pending transactions when it determines that the environment is favorable. In the final results, ROBB successfully minimized the waiting time for transactions and utilized the blocks to their full potential. Additionally, it optimized the block space, building upon the findings of previous researchers. From the research, we can see that our proposed models show impressive results with 100% block utilization and 1.8s average waiting time while creating the least number of blocks.

Published
2024
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2. Reinforcement Learning Methods for OpenAI Environments

Abstract

Using the powerful methods developed in the fieldof reinforcement learning requires an understanding of theadvantages and drawbacks of different methods as well as theeffects of the different adjustable parameters. This paper high-lights the differences in performance and applicability betweenthree different Q-learning methods: Q-table, deep Q-network anddouble deep Q-network where Q refers to the value assigned toa given state-action pair. The performance of these algorithms isevaluated on the two OpenAI gym environments MountainCar-v0 and CartPole-v0. The implementations are done in Pythonusing the Tensorflow toolkit with Keras. The results show thatthe Q-table was the best to use in the Mountain car environmentbecause it was the easiest to implement and was much fasterto compute, but it was also shown that the network methodsrequired far less training data. No significant difference inperformance was found between the deep Q-network and thedouble deep Q-network. In the end, there is a trade-off betweenthe number of episodes required and the computation time foreach episode. The network parameters were also harder to tunesince much more time was needed to compute and visualize theresult., Att använda de kraftfulla metoderna som utvecklats inom området reinforcement learning kräver en förståelse av fördelar och nackdelar mellan olika metoder samt effekterna av de olika justerbara parametrarna. Denna artikel belyser skillnaderna i prestanda och funktionalitet mellan tre olika metoder: Q-table, deep Q-network och double deep Q- network. Prestandan för dessa algoritmer utvärderas i de två OpenAI gym-miljöerna MountainCar-v0 samt Cartpole-v0. Implementeringarna görs i python med hjälp av programvarubiblioteket Tensorflow tillsammans med Keras. Resultaten visar att Q-table var lättast att implementera och tränade snabbast i båda miljöerna. Nätverksmetoderna krävde dock mindre träningsdata även om det tog lång tid att träna på den data som fanns. Inga stora skillnader i prestanda hittades mellan deep Q-network och double deep Q-network. I slutändan kommer det alltid vara en balansgång mellan mängden träningsdata som krävs och tiden det tar att träna på den data som finns.

Published
2020

3. Reinforcement Learning Methods for OpenAI Environments

Abstract

Using the powerful methods developed in the fieldof reinforcement learning requires an understanding of theadvantages and drawbacks of different methods as well as theeffects of the different adjustable parameters. This paper high-lights the differences in performance and applicability betweenthree different Q-learning methods: Q-table, deep Q-network anddouble deep Q-network where Q refers to the value assigned toa given state-action pair. The performance of these algorithms isevaluated on the two OpenAI gym environments MountainCar-v0 and CartPole-v0. The implementations are done in Pythonusing the Tensorflow toolkit with Keras. The results show thatthe Q-table was the best to use in the Mountain car environmentbecause it was the easiest to implement and was much fasterto compute, but it was also shown that the network methodsrequired far less training data. No significant difference inperformance was found between the deep Q-network and thedouble deep Q-network. In the end, there is a trade-off betweenthe number of episodes required and the computation time foreach episode. The network parameters were also harder to tunesince much more time was needed to compute and visualize theresult., Att använda de kraftfulla metoderna som utvecklats inom området reinforcement learning kräver en förståelse av fördelar och nackdelar mellan olika metoder samt effekterna av de olika justerbara parametrarna. Denna artikel belyser skillnaderna i prestanda och funktionalitet mellan tre olika metoder: Q-table, deep Q-network och double deep Q- network. Prestandan för dessa algoritmer utvärderas i de två OpenAI gym-miljöerna MountainCar-v0 samt Cartpole-v0. Implementeringarna görs i python med hjälp av programvarubiblioteket Tensorflow tillsammans med Keras. Resultaten visar att Q-table var lättast att implementera och tränade snabbast i båda miljöerna. Nätverksmetoderna krävde dock mindre träningsdata även om det tog lång tid att träna på den data som fanns. Inga stora skillnader i prestanda hittades mellan deep Q-network och double deep Q-network. I slutändan kommer det alltid vara en balansgång mellan mängden träningsdata som krävs och tiden det tar att träna på den data som finns.

Published
2020

4. Reinforcement Learning Methods for OpenAI Environments

Author

Winberg, Andreas and Öhrstam Lindström, Oliver

Subjects
Reinforcement learning, OpenAIgym, Q-learning, Electrical Engineering, Electronic Engineering, Information Engineering, neural networks, Elektroteknik och elektronik
Abstract

Using the powerful methods developed in the fieldof reinforcement learning requires an understanding of theadvantages and drawbacks of different methods as well as theeffects of the different adjustable parameters. This paper high-lights the differences in performance and applicability betweenthree different Q-learning methods: Q-table, deep Q-network anddouble deep Q-network where Q refers to the value assigned toa given state-action pair. The performance of these algorithms isevaluated on the two OpenAI gym environments MountainCar-v0 and CartPole-v0. The implementations are done in Pythonusing the Tensorflow toolkit with Keras. The results show thatthe Q-table was the best to use in the Mountain car environmentbecause it was the easiest to implement and was much fasterto compute, but it was also shown that the network methodsrequired far less training data. No significant difference inperformance was found between the deep Q-network and thedouble deep Q-network. In the end, there is a trade-off betweenthe number of episodes required and the computation time foreach episode. The network parameters were also harder to tunesince much more time was needed to compute and visualize theresult. Att använda de kraftfulla metoderna som utvecklats inom området reinforcement learning kräver en förståelse av fördelar och nackdelar mellan olika metoder samt effekterna av de olika justerbara parametrarna. Denna artikel belyser skillnaderna i prestanda och funktionalitet mellan tre olika metoder: Q-table, deep Q-network och double deep Q- network. Prestandan för dessa algoritmer utvärderas i de två OpenAI gym-miljöerna MountainCar-v0 samt Cartpole-v0. Implementeringarna görs i python med hjälp av programvarubiblioteket Tensorflow tillsammans med Keras. Resultaten visar att Q-table var lättast att implementera och tränade snabbast i båda miljöerna. Nätverksmetoderna krävde dock mindre träningsdata även om det tog lång tid att träna på den data som fanns. Inga stora skillnader i prestanda hittades mellan deep Q-network och double deep Q-network. I slutändan kommer det alltid vara en balansgång mellan mängden träningsdata som krävs och tiden det tar att träna på den data som finns.

Published
2020

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