Recurrent Off-policy Baselines for Memory-based Continuous Control (2110.12628v1)

Abstract: When the environment is partially observable (PO), a deep reinforcement learning (RL) agent must learn a suitable temporal representation of the entire history in addition to a strategy to control. This problem is not novel, and there have been model-free and model-based algorithms proposed for this problem. However, inspired by recent success in model-free image-based RL, we noticed the absence of a model-free baseline for history-based RL that (1) uses full history and (2) incorporates recent advances in off-policy continuous control. Therefore, we implement recurrent versions of DDPG, TD3, and SAC (RDPG, RTD3, and RSAC) in this work, evaluate them on short-term and long-term PO domains, and investigate key design choices. Our experiments show that RDPG and RTD3 can surprisingly fail on some domains and that RSAC is the most reliable, reaching near-optimal performance on nearly all domains. However, one task that requires systematic exploration still proved to be difficult, even for RSAC. These results show that model-free RL can learn good temporal representation using only reward signals; the primary difficulty seems to be computational cost and exploration. To facilitate future research, we have made our PyTorch implementation publicly available at https://github.com/zhihanyang2022/off-policy-continuous-control.

• The paper introduces a systematic training and evaluation protocol that maintains a 1-to-1 ratio of environment interactions to network updates.
• The paper compares multiple recurrent neural network architectures, showing LSTM's effective mitigation of vanishing gradients and GRU's efficient design.
• The research underscores the importance of consistent hyper-parameter tuning and reproducibility, offering a robust framework for future continuous control studies.

Overview of Recurrent Off-policy Baselines for Memory-based Continuous Control

The paper focuses on the development and evaluation of recurrent off-policy baselines for memory-based continuous control tasks. The work presented involves the assessment and comparison of different recurrent neural network architectures and their efficacy in such tasks, emphasizing the deployment of recurrent architectures over non-recurrent ones.

Key Contributions

• Training and Evaluation Protocol: The paper details a systematic training and evaluation schedule where algorithms were subjected to a regimen of 10 evaluation episodes for every 1000 steps of both environment interactions and network updates. Crucially, a locked 1-to-1 ratio of environment interactions to network updates was maintained.
• Recurrent Neural Network Architectures: The research emphasizes the integration of two recurrent layers with a hidden dimension of 256 to non-recurrent actors and critics, setting a foundation for evaluating recurrent agent architectures. Noteworthy architectures explored include the Elman Network (EN), Long Short-term Memory (LSTM), and Gated Recurrent Unit (GRU).
• Hyper-parameters and Configuration: The paper maintains the consistency of hyper-parameters with widely recognized benchmarks as identified in the field, such as the Stable Baselines3 repository. Adjustments in replay buffer capacity and noise parameters for different algorithms like DDPG, TD3, and SAC are carefully curated to align with observed best practices.

Technical Insights

The paper thoroughly examines several popular recurrent architectures, offering an analytical comparison grounded in the context of memory-based continuous control:

• The LSTM architecture's design is heavily focused on mitigating the vanishing-gradient problem, which is a significant limitation in simpler RNNs like the EN. The complexity of LSTM, including its dual vector state (cell state and hidden state), is noted for its superior performance across diverse tasks.
• Conversely, the GRU, lauded for its simplified architecture compared to LSTM, retains the essential qualities of learning efficiency with a unique configuration of gates, providing a competitive alternative to LSTM.
• Extensive usage of hyper-parameter tuning across models underscores the paper's commitment to rigorous empirical validation. The choice of actors and critics as multi-layer perceptrons points to a strategic baseline setup, facilitating the exploration of the recurrent model’s impact on performance.

Implications and Future Directions

This work lays a foundational blueprint for integrating recurrent structures into off-policy control tasks, offering insights for both existing algorithm enhancement and the development of novel architectures. The portrayal of standard hyper-parameter configurations and fixed ratios of interaction to network updates supports the reproducibility and scalability of this research framework.

The implications extend to the enhancement of agent memory and learning dynamics in real-world scenarios, where continuous control holds substantial practical value, such as robotics and autonomous systems. Future research could explore optimizing recurrent layers' configurations precisely for specific domain tasks, exploring enhanced gating mechanisms or hybrid models combining recurrent and feedforward strategies to address nuanced challenges in continuous control environments. Additionally, further exploration into scalable architectures that incorporate advanced memory elements could propel this research domain into more sophisticated applications.