Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs (2110.05038v3)

Abstract: Many problems in RL, such as meta-RL, robust RL, generalization in RL, and temporal credit assignment, can be cast as POMDPs. In theory, simply augmenting model-free RL with memory-based architectures, such as recurrent neural networks, provides a general approach to solving all types of POMDPs. However, prior work has found that such recurrent model-free RL methods tend to perform worse than more specialized algorithms that are designed for specific types of POMDPs. This paper revisits this claim. We find that careful architecture and hyperparameter decisions can often yield a recurrent model-free implementation that performs on par with (and occasionally substantially better than) more sophisticated recent techniques. We compare to 21 environments from 6 prior specialized methods and find that our implementation achieves greater sample efficiency and asymptotic performance than these methods on 18/21 environments. We also release a simple and efficient implementation of recurrent model-free RL for future work to use as a baseline for POMDPs.

• The paper shows that with meticulous hyperparameter tuning, recurrent model-free RL can match or outperform specialized POMDP methods.
• It emphasizes architectural design, like separating recurrent actor and critic networks, to reduce interference and enhance stability.
• The study finds off-policy techniques such as TD3 and SAC achieve superior sample efficiency across 18 of 21 benchmark environments.

Analysis of Recurrent Model-Free Reinforcement Learning for POMDPs

The paper under examination presents a compelling paper on the applicability and efficacy of recurrent model-free reinforcement learning (RL) as a robust baseline for a broad set of partially observable Markov decision processes (POMDPs). This exploration challenges a widely held belief that dedicated, specialized algorithms outperform such general methods in various POMDP scenarios.

The research revisits previous conclusions that recurrent architectures, in their generality, perform suboptimally when compared to specialized algorithms tailored for specific types of POMDPs. The authors argue that with meticulous architectural design and the careful tuning of hyperparameters, recurrent model-free RL implementations can deliver performance that either matches or even exceeds recent specialized methods. Their findings are substantiated through comparisons across 21 environments sourced from prior specialized methods. Notably, the recurrent model-free approach shows superior sample efficiency and asymptotic performance in 18 of these scenarios.

Crucially, the paper identifies several design considerations for optimizing recurrent model-free RL. The separation of weights between recurrent actor and critic networks mitigates gradient interference, enhancing stability and learning efficiency. The selection of appropriate inputs, including histories of observations and actions, exploits the capacity of recurrent neural networks (RNNs) to capture dependencies obscured by partial observability.

Consistently, the choice of RL algorithm proves pivotal. The paper finds that off-policy algorithms, such as Twin Delayed DDPG (TD3) and Soft Actor-Critic (SAC), are generally more advantageous compared to on-policy counterparts, affording better sample efficiency and performance in most environments tested. The context length within RNNs emerges as a particularly task-specific parameter, where an optimal length balances memory capacity with computational needs.

The work challenges the narrative around RL methodologies, showcasing that a thoughtfully implemented recurrent model-free baseline can be substantial, particularly when consistency across diverse tasks and adaptability to unseen environments are desired. This suggests utility in standard benchmarking, advocating for the employment of recurrent model-free RL as a viable reference point across POMDP applications.

The findings reveal not just a performance baseline but an extensive framework that encourages further research in making design decisions within recurrent architectures more adaptive. They hint at a future avenue whereby automated tuning mechanisms could further potentiate recurrent model-free solutions.

In summary, this research contributes valuable insights into the recurrent model-free RL paradigm, fortifying its position as a competitive baseline for solving diverse POMDPs. The implications are significant, providing a robust foundation for theoretical developments and practical applications in AI and robotics where partial observability remains a challenge.