Large language models (LLMs) have demonstrated high performance on tasks expressed in natural language, particularly in zero- or few-shot settings. These are typically framed as supervised (e.g., classification) or unsupervised (e.g., clustering) problems. However, limited work evaluates LLMs as agents in reinforcement learning (RL) tasks (e.g., playing games), where learning occurs through interaction with an environment and a reward system. While prior work focused on representing tasks that rely on a language representation, we study structured, non-linguistic reasoning - such as interpreting positions in a grid world. We therefore introduce PARL (Prompt-based Agent for Reinforcement Learning), a method that uses LLMs as RL agents through prompting, without any fine-tuning. PARL encodes actions, states, and rewards in the prompt, enabling the model to learn through trial-and-error interaction. We evaluate PARL on three standard RL tasks that do not entirely rely on natural language. We show that it can match or outperform traditional RL agents in simple environments by leveraging pretrained knowledge. However, we identify performance limitations in tasks that require complex mathematical operations or decoding states and actions.

We propose a framework for evaluating strategic deception in large language models (LLMs). In this framework, an LLM acts as a game master in two scenarios: one with random game mechanics and another where it can choose between random or deliberate actions. As an example, we use blackjack because the action space nor strategies involve deception. We benchmark Llama3-70B, GPT-4-Turbo, and Mixtral in blackjack, comparing outcomes against expected distributions in fair play to determine if LLMs develop strategies favoring the "house." Our findings reveal that the LLMs exhibit significant deviations from fair play when given implicit randomness instructions, suggesting a tendency towards strategic manipulation in ambiguous scenarios. However, when presented with an explicit choice, the LLMs largely adhere to fair play, indicating that the framing of instructions plays a crucial role in eliciting or mitigating potentially deceptive behaviors in AI systems.

Blackjack or "21" is a popular card-based game of chance and skill. The objective of the game is to win by obtaining a hand total higher than the dealer's without exceeding 21. The ideal blackjack strategy will maximize financial return in the long run while avoiding gambler's ruin. The stochastic environment and inherent reward structure of blackjack presents an appealing problem to better understand reinforcement learning agents in the presence of environment variations. Here we consider a q-learning solution for optimal play and investigate the rate of learning convergence of the algorithm as a function of deck size. A blackjack simulator allowing for universal blackjack rules is also implemented to demonstrate the extent to which a card counter perfectly using the basic strategy and hi-lo system can bring the house to bankruptcy and how environment variations impact this outcome. The novelty of our work is to place this conceptual understanding of the impact of deck size in the context of learning agent convergence.

Learn the basics of reinforcement learning and how to implement it using Gymnasium (previously called OpenAI Gym). Gymnasium is an open source Python library originally created by OpenAI that provides a collection of pre-built environments for reinforcement learning agents. It provides a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.

As a popular casino card game, many have studied Blackjack closely in order to devise strategies for improving their likelihood of winning. In this project, we will use Reinforcement Learning to find the best playing strategy for Blackjack. We will use Monte Carlo Reinforcement learning algorithms to do it; you will see how Reinforcement Learning can determine the optimum Blackjack strategy in just a few minutes. You will quickly grasp important concepts of Reinforcement learning and apply open AI's gym, the go-to framework for Reinforcement learning. To see all of the detailed explanations for the mentioned concepts and analyze/experiment with the code for this blog. You can also take a lot of FREE courses and projects about data science or any other technology topics from Cognitive Class.

Cyber-attacks continue to threaten organizations large and small. The impacts of a data breach or ransomware attack may have significant and material impacts on both customers and shareholders. To help combat cyber threats, some organizations have started exploring how big data and artificial intelligence (AI) may help to reduce cybersecurity risk. Machine learning algorithms are now common in cybersecurity. We find machine learning offered in more commercial products, from those that are fully integrated into products and require no knowledge of machine learning to those that require rolling up your sleeves to put together the algorithms and perform statistical analysis. Machine learning for cybersecurity has most frequently been applied to detecting patterns that represent attacks. This includes algorithms that evaluate audit log data, that spot anomalies for network intrusion detection systems, and that identify and block malware on computer systems. In some applications, machine learning is used to train models of normal activity on networks in hope of later detecting anomalous events that may represent a cyber-attack.

Cyber-attacks continue to threaten organizations large and small. The impacts of a data breach or ransomware attack may have significant and material impacts on both customers and shareholders. To help combat cyber threats, some organizations have started exploring how big data and artificial intelligence (AI) may help to reduce cybersecurity risk. Machine learning algorithms are now common in cybersecurity. We find machine learning offered in more commercial products, from those that are fully integrated into products and require no knowledge of machine learning to those that require rolling up your sleeves to put together the algorithms and perform statistical analysis. Machine learning for cybersecurity has most frequently been applied to detecting patterns that represent attacks. This includes algorithms that evaluate audit log data, that spot anomalies for network intrusion detection systems, and that identify and block malware on computer systems. In some applications, machine learning is used to train models of normal activity on networks in hope of later detecting anomalous events that may represent a cyber-attack.

Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applications. Efficient implementations of RL techniques could allow for agents deployed in edge-use cases to gain novel abilities, such as improved navigation, understanding complex situations and critical decision making. Towards this goal, we describe a flexible architecture to carry out reinforcement learning on neuromorphic platforms. This architecture was implemented using an Intel neuromorphic processor and demonstrated solving a variety of tasks using spiking dynamics. Our study proposes a usable energy efficient solution for real-world RL applications and demonstrates applicability of the neuromorphic platforms for RL problems.