Since its genesis in late 2008, Bitcoin had a rapid growth in terms of participation, number of transactions and market value. This success is mostly due to innovative use of existing technologies for building a trusted ledger called blockchain. A blockchain system allows its participants (agents) to collectively build a distributed economic, social and technical system where anyone can join (or leave) and perform transactions in-between without needing to trust each other, having a trusted third party and having a global view of the system. It does so by maintaining a public, immutable and ordered log of transactions, which provides an auditable trusted ledger accessible by anyone.However, blockchain systems are environments that are too complex for humans to pre-determine the correct actions using hand-designed solutions. Furthermore, the agents performing in these systems have limited observability, and the state and parameter spaces are vast and changing dynamically. Consequently, agents that can learn to tackle such complex real-world uncertain domains are needed.
Based on this observation, the objective of this thesis is to investigate the uncertain constraints of blockchain systems and to propose a deep reinforcement learning decision-making approach based for all agents like that agents will learn to cooperate in a multi-agent setting in blockchain systems and continuously learn the uncertain constraints.
This thesis seeks to answer the following two research questions:
Concretely, the objective of this thesis is to investigate the uncertain constraints of blockchain systems and to propose a deep reinforcement learning decision-making approach based on utility and rewards for both user and block creator agents.
The thesis will also contribute to develop and extend the agent-based simulation platform Multi-Agent eXperimenter (MAX) of LICIA.
Full description of the thesis can be found here.