Gray-Box Attacks: Threats to Deep Reinforcement Learning in Trading

Deep Reinforcement Learning (Deep RL) has become a useful tool in various fields, including games, self-driving cars, and chat bots. Recently, one of the interesting applications of this technology has been in automated stock trading. However, just like any automated system, Trading Agents can be manipulated by competitors. Therefore, it is necessary to study how these agents can withstand such attacks to ensure their effectiveness in actual trading.

Typically, researchers use a method called white-box attack to analyze the strength of reinforcement learning agents. This means they have complete access to the agent's internal workings. However, in real trading scenarios, trading agents are often protected by secure systems, making such methods impractical. This research focuses on a different approach known as a "gray-box" attack. In this method, an adversary, or competitor, operates in the same trading market without needing direct access to the trading agent's internal details.

#Concept of Gray-box Attacks

A gray-box attack involves an adversary using only the visible information in a trading environment, such as market prices and the trading decisions made by the agent. The study shows that it is possible for an adversary to affect the decision-making of a Deep RL-based trading agent just by participating in the same market.

In this approach, the adversary employs a hybrid deep neural network as its strategy. This type of network includes advanced layers that process information efficiently. Through simulation, it has been found that this adversary can significantly reduce the rewards for the trading agent, which impacts its Profits.

#Significance of Studying Trading Agents' Robustness

Understanding how trading agents respond to adversarial actions is crucial. An adversary can act as a trader and potentially manipulate the market against a specific competitor. Recognizing the vulnerabilities of trading agents is the first step in making them more resilient.

The proposed gray-box framework aims to generate adversarial influences similar to those seen in real stock market conditions. Given that the trading agent's details, like source code and strategy, remain hidden from the adversary, there is a need to find ways to affect the agent based solely on what is observable in the market.

#Deep Reinforcement Learning in Trading

In trading, the problem can be formulated as a Markov Decision Process (MDP). The goal of the trading agent is to maximize profits during trading sessions. The components of this problem include:

• State: This includes details like the agent's remaining cash, shares owned, current share prices, and various indicators that help in decision-making.
• Action: The choices the agent can make, such as buying, selling, or holding stocks.
• Reward: A measurement of the agent's success in achieving its goals based on its decisions.
• Policy: A deep neural network that helps the agent decide the best action based on the current state.

Several popular algorithms are available for Deep RL applications in trading. These usually fall into different categories, such as actor-critic methods, which involve using two networks to learn simultaneously. One network predicts the best action, while the other estimates the expected rewards.

#The Vulnerability of Trading Agents

Despite the advancements in these algorithms, trading agents can still be influenced by adversarial actions. Past studies have shown that Deep RL agents are vulnerable to adversarial examples, which can lead to incorrect decisions. Many of these earlier studies on agent robustness involved situations where the attacker had direct access to the inputs or internal workings of the agent.

However, in real-world trading scenarios, this level of access is practically impossible. Instead, it is possible to develop a method where the adversary interacts with the trading environment much like another player. The goal is to use these interactions to influence the trading agent's decisions without direct manipulation.

#Implementing the Adversary Approach

The goal here is to create an adversarial approach that affects Deep RL trading agents within an environment that mimics real trading conditions. The adversary does not have access to any internal details of the victim trading agent but can observe the trading environment and the agent's public decision making.

A trading market simulation called ABIDES is used to test this framework. This simulation allows for a dynamic environment where different agents can trade, much like in a real stock market. During experiments, the adversarial agent was designed to make trades based on observable information.

This means it has to develop strategies that can impact the decision-making process of the trading agents. The success of this adversarial policy can be evaluated using several research questions.

#Research Questions

1. Effectiveness of the Adversary: How well can the proposed adversary impact the decisions made by the trading agents?
2. Profit Impact: To what extent can the adversary change the profits of the trading agents?
3. Cost of Attack: How effectively can the adversary manipulate the trading agent without incurring excessive costs?

#Experimental Evaluation

The proposed approach goes through several evaluations using different trading agents. These include a baseline agent, an ensemble agent, and an industrial agent. Each agent functions differently, with the aim of assessing how well the adversary can influence their decisions and profits.

The first aspect to explore is the effectiveness of the adversarial agent in altering the trading agent's decisions. This involves directly comparing the outputs of the trading agent before and after the adversary's presence. The evaluation focuses on whether the adversary can change the decision-making process, ensuring that the trading agent starts making less profitable trades.

Next, the evaluation looks at the impact on profits. Here, the trading agent's returns are examined during trading sessions with and without the adversary. This provides insight into the adversary's success in compelling the trading agent to make less beneficial choices over time.

Lastly, the research investigates the resource usage of the adversary. Successful manipulation does not just rely on effectiveness but also on the cost incurred while trading. The goal is for the adversary to impose profit losses on the trading agent while maintaining a reasonable cost for its own operations.

#Results and Findings

The results from these experiments indicate that the proposed adversarial method can significantly disrupt the normal functions of the trading agents.

• Adversarial Impact on Decision Making: The trading agents showed a notable drop in their average rewards under the influence of the adversary. This suggests that the adversary was successful in forcing the trading agents to make incorrect trades.

• Reduction in Profits: The experiments revealed that the adversary could effectively decrease the returns of the trading agents. The amount of profit loss varied based on which trading agent was being attacked, but overall, the adversarial actions led to significant financial impacts.

• Resource Management: While the adversary was able to cause considerable losses to the trading agents, it achieved this by using less of its own resources than what the victims lost.

#Implications for Trading Systems

The findings from this research carry important implications for the development of trading systems. As trading technology becomes more advanced, so do the methods of competitors looking to exploit weaknesses. Understanding how adversarial actions can impact automated trading agents is essential for creating more robust and reliable systems.

Future work could focus on using insights from this research to develop defensive methods against Adversaries. Another avenue for exploration could involve training agents to detect and alert trading systems about potential threats in real-time.

In conclusion, this study contributes to a better understanding of the interactions between trading agents and adversaries in a simulated trading environment. By examining these dynamics, it becomes possible to improve the resilience of automated trading systems, ensuring they can perform efficiently in increasingly competitive settings.