IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures (1802.01561v3)

Abstract: In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al., 2016)) and Atari-57 (all available Atari games in Arcade Learning Environment (Bellemare et al., 2013a)). Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach.

• The paper introduces IMPALA, a novel architecture that decouples actors and learners to enhance scalability and efficiency in deep reinforcement learning.
• It proposes the V-trace algorithm for off-policy correction, ensuring stable convergence even when using outdated trajectory data.
• Empirical results demonstrate up to 30x throughput on DeepMind Lab and significant improvements in multi-task benchmarks like DMLab-30 and Atari-57.

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Introduction

The paper "IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures" introduces a reinforcement learning (RL) framework designed for scalable and efficient multi-task training. Recognizing the computational inefficiencies inherent in prior RL architectures like A3C and UNREAL, the authors present IMPALA (Importance Weighted Actor-Learner Architecture), a distributed agent leveraging a novel off-policy correction method termed V-trace.

Core Contributions

The paper's primary contributions can be divided into two main components: the IMPALA architecture and the V-trace algorithm.

IMPALA Architecture:

• Decoupled Actor and Learner: Unlike architectures where workers communicate gradients, IMPALA segregates actors that generate trajectories from learners that perform updates, thereby ensuring stable learning and high data throughput.
• Scalability: The architecture is specifically designed to scale to thousands of machines. By processing batches of trajectories asynchronously, IMPALA achieves remarkable data throughput rates.
• GPU Utilization: The framework utilizes GPUs efficiently by parallelizing large time-independent operations. This optimization enables the learner to execute updates swiftly on extensive neural network architectures.

V-trace Algorithm:

• Off-Policy Correction: V-trace addresses the issue of the policy-lag between actors and learners, where trajectories generated by actors might be based on outdated policies. The algorithm corrects this lag using truncated importance sampling weights, ensuring stable and efficient off-policy learning.
• Convergence and Stability: The paper proves V-trace to be a contraction mapping with a unique fixed point, ensuring convergence to the value function of the truncated importance-weighted policy.

Key Results

The practical implications of IMPALA and V-trace are evaluated through extensive empirical results on single-task and multi-task environments:

Single-Task Training:

• DeepMind Lab: When trained on diverse DeepMind Lab tasks, IMPALA exhibits superior data efficiency and stability compared to A3C and batched A2C variants. Notably, IMPALA achieves up to 30 times the throughput of single-machine A3C, reaching 250,000 frames per second on a distributed setup.

Multi-Task Training:

• DMLab-30 and Atari-57: IMPALA's most pronounced advantages manifest in multi-task settings. For DMLab-30, an aggregate suite of 30 tasks, IMPALA achieves a mean capped human-normalized score of 49.4%, far surpassing the 23.8% achieved by A3C. Similarly, on Atari-57, IMPALA's multi-task agent achieves a 59.7% median human-normalized score, underscoring its capability to leverage positive transfer across varied tasks.

Implications and Future Directions

The findings from this paper not only highlight the significant step forward in efficiently training RL agents on scalable architectures but also pave the way for several future research directions:

1. Hyperparameter Optimization: The robustness of V-trace across various hyperparameter settings suggests potential for more automated and adaptive hyperparameter tuning methods, including meta-learning approaches.
2. Transfer Learning in RL: The positive transfer observed in multi-task settings indicates avenues for further exploring transfer learning paradigms, where knowledge from simpler tasks can accelerate learning in more complex environments.
3. Real-time Applications: The substantial improvement in data throughput and computational efficiency makes IMPALA a prime candidate for real-time RL applications, from autonomous driving to real-time simulation environments.

Conclusion

The paper provides a meticulous analysis and extensive empirical validation of IMPALA and V-trace, positioning them as robust tools in the reinforcement learning arsenal. The focus on scalable architecture, combined with principled off-policy correction, marks significant progress towards practical and efficient multi-task learning in RL. Future research should build upon these findings to further generalize and apply these techniques to broader, more complex problem domains.