VQEL: Enabling Self-Developed Symbolic Language in Agents through Vector Quantization in Emergent Language Games (2503.04940v1)

Abstract: In the field of emergent language, efforts have traditionally focused on developing communication protocols through interactions between agents in referential games. However, the aspect of internal language learning, where language serves not only as a communicative tool with others but also as a means for individual thinking, self-reflection, and problem-solving remains underexplored. Developing a language through self-play, without another agent's involvement, poses a unique challenge. It requires an agent to craft symbolic representations and train them using direct gradient methods. The challenge here is that if an agent attempts to learn symbolic representations through self-play using conventional modeling and techniques such as REINFORCE, the solution will offer no advantage over previous multi-agent approaches. We introduce VQEL, a novel method that incorporates Vector Quantization into the agents' architecture, enabling them to autonomously invent and develop discrete symbolic representations in a self-play referential game. Following the self-play phase, agents can enhance their language through reinforcement learning and interactions with other agents in the mutual-play phase. Our experiments across various datasets demonstrate that VQEL not only outperforms the traditional REINFORCE method but also benefits from improved control and reduced susceptibility to collapse, thanks to the incorporation of vector quantization.

• The paper introduces VQEL, a novel method using vector quantization in a self-play phase to enable agents to develop internal, discrete symbolic languages before mutual interaction.
• Experiments show VQEL consistently outperforms the traditional REINFORCE baseline in generating robust and discriminative language across various datasets and communication capacities.
• VQEL's self-play phase provides significant advantages in training stability, control over learned representations, and reduced susceptibility to collapse compared to standard RL approaches.

VQEL: Vector Quantization for Emergent Language in Agents

The paper "VQEL: Enabling Self-Developed Symbolic Language in Agents through Vector Quantization in Emergent Language Games" (2503.04940) introduces a novel approach, VQEL, to address the challenge of enabling agents to develop internal, symbolic languages through self-play in emergent communication. This contrasts with traditional emergent language research that primarily focuses on inter-agent communication protocols developed through referential games. VQEL leverages vector quantization (VQ) within the agent's architecture to facilitate the creation and learning of discrete symbolic representations in a self-supervised manner. After the self-play phase, the developed language is further refined through reinforcement learning (RL) and interaction with other agents in a mutual-play phase.

Motivation and Problem Statement

The core problem addressed by VQEL is the difficulty in creating transferable, discrete, symbolic languages when agents learn through self-play. Standard approaches using continuous internal representations lack the desired symbolic properties. Moreover, directly applying RL techniques like REINFORCE in a self-play loop suffers from similar instability and high variance issues as observed in multi-agent RL, negating any potential advantage. The VQEL method aims to overcome these limitations by introducing a mechanism for agents to autonomously invent and develop discrete symbolic representations. This allows for a more controlled and stable learning process compared to traditional RL-based methods.

Method: Vector Quantization Emergent Language (VQEL)

VQEL integrates Vector Quantization into the agent's architecture, particularly within the Text Generation Module. The method comprises two main phases: a self-play phase for initial language development and a mutual-play phase for refinement through interaction.

Self-Play Phase

During self-play, an agent interacts with itself in a referential game. The process involves the following key steps:

1. Encoding: The agent encodes an object, generating a sequence of internal representations using a recurrent neural network (RNN).
2. Vector Quantization: The continuous hidden states from the RNN are transformed into a vector z, which is then mapped to the nearest vector in a learned discrete codebook C via VQ. The index w_t of the closest codebook vector c_wt serves as the discrete symbol generated at each time step. This is an argmax operation, which can be made differentiable using the straight-through estimator.
3. Training: The entire self-play loop, encompassing object perception, text generation (including VQ), and text perception, is trained end-to-end using a contrastive loss (similar to CLIP) and a VQ-specific commitment loss. This avoids the need for RL during the initial language invention phase. The codebook vectors are updated using an exponential moving average (EMA) based on the z vectors that map to them. Techniques such as expiring stale codes are employed to ensure efficient codebook utilization.
4. Transferable Language: The discrete symbols learned in the self-play phase are inherently transferable.

Mutual-Play Phase

Following self-play, the agent engages in a standard referential game with another agent. This phase allows for the refinement and standardization of the self-developed language through interaction.

1. Symbol Generation: The sender agent generates the learned symbols.
2. Interpretation: The receiver agent interprets the symbols.
3. Refinement: The sender agent is further fine-tuned using REINFORCE based on the receiver's success, while the receiver learns via the contrastive loss.

How Vector Quantization Enables Symbolic Language

Vector Quantization is critical to VQEL's ability to create self-developed symbolic language. It enforces discreteness on the agent's internal representations, facilitating the emergence of symbolic units. The process is detailed below:

1. Discretization: An RNN produces continuous hidden states, which are then mapped to a vector z through a transformation layer.
2. Codebook Lookup: VQ compares z to all vectors (c_k) in a finite codebook C, identifying the closest vector c_wt. The index w_t of this vector becomes the discrete symbol.
3. Embedding Output: The chosen codebook vector e_wt = c_wt is used as input for the subsequent step in the RNN.
4. Gradient Flow: During self-play, the gradient from the loss function can flow back through the VQ layer (using the straight-through estimator) to update the RNN, the transformation layer, and the object perception module.
5. Codebook Learning: The codebook vectors are updated using an EMA based on the z vectors that map to them, and a commitment loss encourages the z vectors to remain close to their chosen codebook vectors.

Experimental Results

The paper presents experimental results comparing VQEL (with self-play and mutual-play phases) against a standard REINFORCE baseline (trained only via mutual-play) on three datasets: Synthetic Objects, DSprites, and CelebA. The experiments were conducted under two different communication channel capacities (Vocab=10, Length=4 and Vocab=5, Length=3).

Key Findings

The experiments demonstrated the following key findings:

1. VQEL Outperforms REINFORCE: Across all datasets and settings, the final VQEL model (Self-Play + Mutual-Play) consistently achieved higher accuracy than the baseline REINFORCE method. This was particularly evident when tested with a larger number of distractors (e.g., Acc C32, Acc C100, Acc C1000), indicating that the language developed by VQEL is more robust and discriminative.
2. Effectiveness of Self-Play: The language developed solely through the self-play phase of VQEL often exhibited strong performance, sometimes even surpassing the baseline REINFORCE model trained for the same duration.
3. Channel Capacity Utilization: In the higher-capacity setting (V=10, L=4), VQEL methods generated a larger number of unique messages compared to REINFORCE, indicating better utilization of the available communication bandwidth.
4. Hyperparameter Sensitivity: The commitment loss weight in VQ was crucial. Values too close to zero hindered training, while very high values could limit exploration during the mutual-play (REINFORCE) phase.

Advantages over Traditional RL (REINFORCE)

VQEL offers several advantages over traditional RL-based approaches like REINFORCE:

1. Training Stability: VQEL's self-play phase uses direct gradient descent (backpropagation), which provides more stable training and lower variance compared to the REINFORCE algorithm.
2. Initial Language Grounding: VQEL allows an agent to establish a grounded, discrete symbolic system before interacting with another agent.
3. Control and Reduced Collapse: The paper highlights improved control and reduced susceptibility to collapse as benefits of VQEL, attributed to the VQ mechanism. The structured nature of VQ and the use of direct gradients in self-play contribute to more controlled and stable learning. Techniques like the commitment loss and expiring stale codes prevent the VQ codebook from collapsing.

Conclusion

VQEL presents a method for agents to develop discrete, symbolic languages through internal monologue, leveraging the differentiability and structure provided by Vector Quantization. The results demonstrate that this approach leads to more effective and robust emergent languages compared to traditional REINFORCE-based dialogue methods, offering benefits in training stability and control over learned representations.