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  • Writer's pictureBella Callaway

Unlocking the Potential of Reinforcement Learning: Improving Trust in Artificial Intelligence

Artificial Intelligence (AI) is revolutionising the way we interact with technology, pushing the boundaries of what machines can achieve. At its core, AI is a simulated intelligence on programmable machines, aiming to mimic the intricate workings of the human brain. Within this vast field, Machine Learning (ML) emerges as a sub-field, focusing on the development of software agents that self-improve their functioning with experience.


Within Machine Learning, one particularly fascinating area is Reinforcement Learning (RL). This sub-field empowers AI-based systems to navigate dynamic environments, learning through trial and error to maximise cumulative rewards based on feedback received for individual actions. In essence, RL allows software agents to interact with unknown environments, selecting actions, and progressively uncovering the dynamics of the new environment.


Reinforcement Learning Model via Spiceworks.

In the above figure, a computer may represent an agent in a particular state (St). It acts (At) in an environment to achieve a specific goal. As a result of the performed task, the agent receives feedback as a reward or punishment (R).


In practice RL centred around rewarding desired behaviours and penalising negative ones. This method assigns positive values to actions that lead to desired outcomes, incentivising the agent to replicate them. Conversely, negative values are assigned to undesired behaviours, discouraging their repetition. This approach mirrors the way humans learn from experience, making it a powerful tool for optimising AI-driven systems. What sets RL apart from other methods of ML is its ability to optimise AI systems without explicit programming, Instead, it mimics natural intelligence, emulating human cognition to make critical decisions. This autonomous learning approach enables computer agents to achieve astounding results in various tasks, all without direct human intervention.


The real-world applications of RL are vast and diverse. From optimising resource allocation in logistics to enhancing recommendation systems in service industries, RL has proven its efficacy across a range of industries. Its ability to adapt and learn from experience makes it particularly well-suited for tasks where environments are dynamic and complex. Moreover, RL holds the promise of unlocking new frontiers in AI research. As algorithms become more sophisticated and computing power continues to advance, we can expect even greater strides in autonomous decision-making and problem-solving.


RL algorithms often require extensive computational resources, particularly for tasks with large state and action spaces. Training RL agents can be time-consuming and computationally intensive, limiting their scalability to real-world applications. Additionally, RL algorithms typically learn from experience through trial and error, this can be inefficient especially in environments where obtaining feedback is costly or time consuming. This sample inefficiency can slow down the learning process and hinder the practical applicability of RL in certain domains. In order to have trust in systems utilising RL technology, there must transparency at all stages of implementation.


THEMIS 5.0 aims to leverage RL to advance human-AI enabled trustworthiness improvement by placing the user’s utility at centre stage of the decision-making process in the THEMIS ecosystem. In THEMIS an interactive RL agent will user human evaluative feedback, for example evaluations of the quality of the agent’s behaviour provided by a human user, to continuously improve and aid the process of computationally modelling human learning and decision making. The use of RL is key in the development of the THEMIS human-centred AI ecosystem as it provides an invaluable avenue through which human-centred objective can specifically guide the development of the THEMIS model.


Reinforcement Learning stands as a powerful tool in the realm of Artificial Intelligence. By harnessing the principles of reward and punishment, RL enables AI systems to learn and adapt autonomously, mirroring the way humans learn from experience. As we continue to explore the potential of RL, we can expect to see ground-breaking innovations that redefine the possibilities of AI-driven technology.


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