POPGym: Benchmarking Partially Observable Reinforcement Learning (2303.01859v1)

Abstract: Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory. Despite this, partial observability is still largely ignored by contemporary RL benchmarks and libraries. We introduce Partially Observable Process Gym (POPGym), a two-part library containing (1) a diverse collection of 15 partially observable environments, each with multiple difficulties and (2) implementations of 13 memory model baselines -- the most in a single RL library. Existing partially observable benchmarks tend to fixate on 3D visual navigation, which is computationally expensive and only one type of POMDP. In contrast, POPGym environments are diverse, produce smaller observations, use less memory, and often converge within two hours of training on a consumer-grade GPU. We implement our high-level memory API and memory baselines on top of the popular RLlib framework, providing plug-and-play compatibility with various training algorithms, exploration strategies, and distributed training paradigms. Using POPGym, we execute the largest comparison across RL memory models to date. POPGym is available at https://github.com/proroklab/popgym.

• The paper presents POPGym, a benchmark focused on addressing memory challenges in partially observable reinforcement learning tasks.
• It employs 15 diverse POMDP environments and 13 baseline memory models evaluated with PPO to showcase performance and computational efficiency.
• The study underscores the necessity of memory in real-world RL and paves the way for advancing novel architectures for complex decision-making.

Overview of "POPGym: Benchmarking Partially Observable Reinforcement Learning"

The paper "POPGym: Benchmarking Partially Observable Reinforcement Learning" introduces a benchmark specifically aimed at addressing the gap in reinforcement learning (RL) evaluations concerning partially observable environments. It emphasizes the need for memory in solving real-world RL tasks by proposing Partially Observable Process Gym (POPGym). This contribution is twofold: a selection of 15 diverse partially observable environments with multiple difficulty levels, and an implementation of 13 memory model baselines.

A core observation in this work is that existing benchmarks predominantly focus on fully observable environments based on Markov Decision Processes (MDPs), which do not necessitate memory. When it comes to Partially Observable MDPs (POMDPs), memory becomes a crucial aspect due to the incomplete, ambiguous, or noisy nature of observations. The authors outline that contemporary RL libraries provide limited memory model support, largely restricted to frame stacking and recurrent neural networks (RNNs), such as Long Short-Term Memory (LSTM).

Contributions and Features

1. Diverse Environments: POPGym consists of 15 environments classified into diagnostic, control, noisy, game, and navigation categories. This classification ensures a comprehensive benchmark covering multiple POMDP facets, including memory capacity and duration. Each environment is uniquely configured to challenge various memory model aspects, such as the ability to summarize past observations or to integrate noisy and partial data into effective decision-making processes.
2. Memory Models: The baseline memory models implemented represent a wide computational range of models from different sequence prediction paradigms, including classical RNNs, attention-based methods, and convolutional networks. Key models include GRUs, Fast Autoregressive Transformers (FART), and Differentiable Neural Computers (DNCs), showcasing an extensive array of both classic and modern approaches to sequence modeling in RL.
3. Computational Efficiency: The environments produce low-dimensional observations, which use less memory and compute compared to pixel-based approaches. This results in many baseline models converging with two hours of training on a consumer-grade GPU, making the benchmark accessible for widespread experimentation.
4. Evaluation Using PPO: The paper employs Proximal Policy Optimization (PPO) to evaluate memory models across the various POPGym tasks, culminating in detailed performance comparisons. The authors provide comprehensive tables and figures illustrating throughput and convergence data, elucidating the relative performance and efficiency of different models.

Implications and Future Considerations

The introduction of POPGym facilitates an evolved focus on memory-intensive RL applications, broadening the understanding and development of algorithms capable of solving POMDPs efficiently. As interdisciplinary fields like robotics and autonomous navigation increasingly rely on RL solutions, benchmarks like POPGym can drive advancements in deploying RL models that can handle real-world complexity and uncertainty effectively.

Moving forward, several insights from the framework should guide future research trajectories:

• Exploration Beyond Existing Models: While contemporary RNNs such as GRUs performed admirably, the research underlines a dissonance between supervised learning performances and RL outcomes, inviting further investigation into novel architectures that amalgamate SL success with RL applicability.
• Algorithmic Diversity: Although PPO was utilized for evaluations, expanding this selection to include alternative algorithms could refine understanding of the interaction between task characteristics and algorithm performance.
• Task Complexity and Dynamics: As certain navigation tasks were found to be solvable without extensive memory, enhancing the complexity or altering the reward structures within benchmarks may further emphasize memory utilization. Testing models on such challenging dynamics could stress-test the inventive memory architectures proposed by future works.

Ultimately, POPGym serves as a pivotal benchmark that encourages proactive RL research in partially observable scenarios, underscoring the critical importance of memory in complex decision-making processes. This paper is poised to be a cornerstone reference for further investigations seeking to unravel and improve the state of memory-based reinforcement learning.