Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning (2106.03400v2)

Abstract: Learning from datasets without interaction with environments (Offline Learning) is an essential step to apply Reinforcement Learning (RL) algorithms in real-world scenarios. However, compared with the single-agent counterpart, offline multi-agent RL introduces more agents with the larger state and action space, which is more challenging but attracts little attention. We demonstrate current offline RL algorithms are ineffective in multi-agent systems due to the accumulated extrapolation error. In this paper, we propose a novel offline RL algorithm, named Implicit Constraint Q-learning (ICQ), which effectively alleviates the extrapolation error by only trusting the state-action pairs given in the dataset for value estimation. Moreover, we extend ICQ to multi-agent tasks by decomposing the joint-policy under the implicit constraint. Experimental results demonstrate that the extrapolation error is successfully controlled within a reasonable range and insensitive to the number of agents. We further show that ICQ achieves the state-of-the-art performance in the challenging multi-agent offline tasks (StarCraft II). Our code is public online at https://github.com/YiqinYang/ICQ.

• The paper introduces ICQ, a novel approach that reduces extrapolation error by relying solely on observed state-action pairs through a supervised regression framework.
• The paper extends ICQ to multi-agent environments by decomposing joint policies to efficiently manage the exponential complexity of large state and action spaces.
• Experiments in challenges like StarCraft II validate ICQ's superior Q-value accuracy and robust scalability compared to existing offline reinforcement learning methods.

Implicit Constraint Approach for Multi-Agent Offline Reinforcement Learning

The paper "Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning" presents a novel approach to addressing the challenges of extrapolation error in offline multi-agent reinforcement learning (MARL). The authors introduce Implicit Constraint Q-learning (ICQ), which effectively manages extrapolation error by relying solely on observed state-action pairs for value estimation. This approach stands out as existing offline RL algorithms struggle with the complexity introduced by multi-agent environments due to the large state and action spaces.

Key Contributions

1. Implicit Constraint Q-learning (ICQ): ICQ is proposed to mitigate the extrapolation error inherent in offline RL through an implicit constraint optimization. It utilizes a SARSA-like algorithm and converts policy learning into a supervised regression problem, thus avoiding the use of out-of-distribution (OOD) state-action pairs in Q-value estimation.
2. Extension to Multi-Agent Tasks: The authors extend ICQ to multi-agent environments by decomposing the joint-policy under the implicit constraint framework. The decomposition aids in managing the complexity of multi-agent systems, where action spaces grow exponentially.
3. Theoretical Analysis: The paper provides a theoretical elucidation of extrapolation error propagation in offline MARL. The authors demonstrate the impact of unseen state-action pairs and propose analytical models to quantify the propagation of extrapolation errors. They establish that the error propagation is proportional to the transition matrix size and is significantly exacerbated by larger action spaces.
4. Experimental Validation: ICQ showcases state-of-the-art performance in multi-agent offline tasks, particularly challenging environments like StarCraft II. The method curbs extrapolation error within a reasonable range, making it robust and scalable across varying numbers of agents.

Empirical Results

Empirical evaluations underscore ICQ's prowess in controlling extrapolation error across diverse multi-agent scenarios, demonstrating insensitivity to the increasing number of agents. Contrasting the performance of ICQ with existing methods like Batch-Constrained deep Q-learning (BCQ), ICQ consistently delivers superior accuracy in Q-value estimation, particularly as agent numbers increase.

The results highlight ICQ's effectiveness in the StarCraft II multi-agent benchmark, where it substantially outperforms baseline algorithms such as QMIX, BCQ-MA, CQL-MA, and BC-MA. Additionally, single-agent experiments in the D4RL benchmark further affirm ICQ's capability to handle both discrete and continuous control tasks efficiently.

Implications and Future Directions

The Implicit Constraint Q-learning approach has significant implications for offline MARL applications, particularly in domains with complex interactions and large-scale agent systems such as autonomous driving, signal processing, and intelligent transportation systems. By successfully addressing extrapolation errors, ICQ paves the way for deploying MARL solutions in practical, risk-averse environments.

Future research could explore enhancements in value decomposition frameworks, potentially allowing finer-grained control over agent interactions within MARL systems. The robustness of ICQ against data quality deterioration suggests promising avenues for its application in real-world scenarios where data may be noisy or limited. Continued advancements in adaptive learning methods will bolster the integration of offline RL across increasingly sophisticated multi-agent systems.