Deep Transformer Q-Networks for Partially Observable Reinforcement Learning (2206.01078v2)

Abstract: Real-world reinforcement learning tasks often involve some form of partial observability where the observations only give a partial or noisy view of the true state of the world. Such tasks typically require some form of memory, where the agent has access to multiple past observations, in order to perform well. One popular way to incorporate memory is by using a recurrent neural network to access the agent's history. However, recurrent neural networks in reinforcement learning are often fragile and difficult to train, susceptible to catastrophic forgetting and sometimes fail completely as a result. In this work, we propose Deep Transformer Q-Networks (DTQN), a novel architecture utilizing transformers and self-attention to encode an agent's history. DTQN is designed modularly, and we compare results against several modifications to our base model. Our experiments demonstrate the transformer can solve partially observable tasks faster and more stably than previous recurrent approaches.

• The paper introduces the DTQN architecture that leverages transformer decoders and self-attention to encode an agent’s history in partially observable environments.
• The paper presents an intermediate Q-value prediction strategy that enhances training robustness and reduces dependency on recurrent networks.
• The paper demonstrates DTQN’s superior performance and stability via thorough ablation studies and comparisons against conventional DQN-based models.

Insights into Deep Transformer Q-Networks for Partially Observable Reinforcement Learning

In the paper titled "Deep Transformer Q-Networks for Partially Observable Reinforcement Learning," the authors address the significant challenge posed by partially observable environments in reinforcement learning (RL). Traditional Deep Q-Networks (DQNs) and similar approaches typically assume full observability of the environment's state, an assumption that does not hold in many real-world situations. This paper introduces the Deep Transformer Q-Network (DTQN) as a robust alternative capable of handling such scenarios.

Core Contributions

The primary contribution of the paper is the DTQN, a novel architecture that utilizes transformers and self-attention mechanisms to effectively encode an agent's history of observations. This approach contrasts with the more traditional use of recurrent neural networks (RNNs), such as LSTMs and GRUs, which often struggle with stability and training complexities in RL tasks. DTQNs leverage the power of the transformer decoder architecture, integrating self-attention to model sequences better and predict Q-values at each time step of the agent's observation history.

Methodological Innovations

1. Transformer Structure in RL: The paper discusses how the transformer decoder can be adapted for reinforcement learning. The authors advocate for using learned positional encodings within the transformer, allowing the model to adapt to various temporal dependencies in the environment.
2. Intermediate Q-Value Prediction: Unlike traditional approaches that might train based on the last Q-value prediction, DTQN incorporates a training strategy called intermediate Q-value prediction. This involves training on all the Q-values generated throughout the agent's observation history, which enhances robustness and learning efficiency.
3. Ablation Studies and Comparisons: The research includes thorough ablation studies comparing DTQN against various baselines such as DQN, DRQN, DARQN, and ADRQN. Furthermore, they explore different variations of DTQN by modifying components like the position of LayerNorm and the combination step, which in some versions employ GRU-like gating.

Experimental Evaluation

DTQN was evaluated across a suite of challenging partially observable domains, including classic POMDP tasks (like Hallway and HeavenHell), Gridverse domains, and novel environments such as Memory Cards. The results demonstrated that DTQN achieved superior performance in both learning speed and success rates compared to existing baselines. Notably, DTQN was able to maintain stability and high performance where other architectures struggled or required additional architectural tweaks.

Practical and Theoretical Implications

The implications of this research stretch across various domains where partial observability is a significant concern. By integrating transformers into reinforcement learning, the paper suggests a pathway for future work to explore more complex dependencies and histories in RL environments. The use of self-attention mechanisms in particular highlights potential advancements in effectively modeling sequences, which could be extended to multi-agent systems or real-time dynamic environments.

Anticipated Future Developments

Future research could explore the application of DTQNs in more complex and dynamic real-world environments, potentially integrating with more advanced transformer architectures like the TransformerXL for handling longer sequences efficiently. Additionally, the explainability feature offered by attention mechanisms could be further developed to provide more transparent decision-making processes in RL agents.

In summary, "Deep Transformer Q-Networks for Partially Observable Reinforcement Learning" presents a compelling and methodologically sound approach to handling the challenges posed by partial observability in RL, setting a foundation for further exploration and development in leveraging transformer-based architectures within this domain.