Opponent Modeling in Deep Reinforcement Learning (1609.05559v1)

Abstract: Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challenging because strategies interact with each other and change. Most previous work focuses on developing probabilistic models or parameterized strategies for specific applications. Inspired by the recent success of deep reinforcement learning, we present neural-based models that jointly learn a policy and the behavior of opponents. Instead of explicitly predicting the opponent's action, we encode observation of the opponents into a deep Q-Network (DQN); however, we retain explicit modeling (if desired) using multitasking. By using a Mixture-of-Experts architecture, our model automatically discovers different strategy patterns of opponents without extra supervision. We evaluate our models on a simulated soccer game and a popular trivia game, showing superior performance over DQN and its variants.

• The paper introduces DRON, a novel architecture that integrates opponent behavior modeling with policy learning in deep reinforcement learning.
• It leverages two approaches—DRON-Concat and DRON-Mixture-of-Experts—to capture diverse opponent strategies without relying on domain-specific knowledge.
• Experimental results in soccer and Quiz Bowl games demonstrate DRON’s superiority over traditional DQN models in adapting to dynamic multi-agent environments.

Opponent Modeling in Deep Reinforcement Learning

The paper "Opponent Modeling in Deep Reinforcement Learning" by He, Boyd-Graber, Kwok, and Daumé III addresses the challenge of opponent modeling in multi-agent reinforcement learning (RL) environments. While traditional RL focuses on optimizing a single agent's decision-making strategy within a stationary environment, this work extends the RL framework to consider dynamic multi-agent scenarios where the strategies of opponents evolve over time.

Summary and Methodology

The research presented in this paper advocates for a generalized opponent modeling framework within RL that does not rely heavily on domain-specific knowledge. Traditional methods have used probabilistic models or parameterized strategies honed for specific games like poker. In contrast, this paper utilizes deep reinforcement learning to create neural-based models that simultaneously learn an agent's strategy and the behaviors of its opponents.

DRON Architecture

Central to the paper is the Deep Reinforcement Opponent Network (DRON), which integrates policy learning and opponent modeling into a single unified architecture. The DRON framework builds upon the Deep Q-Network (DQN) architecture to introduce two key network components: the policy learning module that computes Q-values and the opponent learning module that infers hidden opponent strategies. The paper explores two architectural variations:

1. DRON-Concat: This model combines state and opponent representations by concatenating them, with the combined vector used to predict Q-values. This approach requires an expressive representation of opponent behavior to account for varied playing strategies.
2. DRON-Mixture-of-Experts (MoE): This model addresses the interactions between an agent's actions and its opponents' strategies using a Mixture-of-Experts network that models opponent actions as latent variables. Experts, each representing different opponent behaviors, predict Q-values weighted by a gating network trained on opponent representations.

The DRON is adaptable, allowing additional supervision and explicit modeling of opponent strategies, which is achieved through a multitask learning component.

Experimental Results

The empirical evaluation of DRON was conducted in two distinct settings: a simulated soccer game and a trivia game (Quiz Bowl). Both environments offered dynamic opponent strategies that required the primary agent to adapt:

• Soccer Game: The DRON models outperformed traditional DQN models by modeling the behavior of opponents rather than treating them as part of the environment. The robustness to varying opponent strategies was particularly notable, with DRON-MoE demonstrating effectiveness without requiring prior knowledge of the optimal strategy set.
• Quiz Bowl: This environment demonstrated DRON's ability to optimize the trade-off between accuracy and buzzer speed. Here, integrating opponent models allowed the agent to anticipate buzz positions relative to the strategies of human players, leading to more effective gameplay.

Implications and Future Directions

The contributions of this paper are twofold: it provides a domain-independent method for opponent modeling, and it offers a framework that can seamlessly adapt to multi-agent settings with evolving strategies. The DRON architecture leverages recent advances in deep reinforcement learning, showing substantial improvement over traditional methods.

Future work could explore the incorporation of more sophisticated neural architectures, such as deep Mixture-of-Experts, to enhance scalability and generalization. Furthermore, optimizing online adaptability in environments with rapidly shifting opponent behaviors remains a promising avenue for research.

In conclusion, the paper underscores the potential for deep reinforcement learning to transform opponent modeling across a broad range of strategic multi-agent applications.