Distributed Prioritized Experience Replay (1803.00933v1)

Abstract: We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a fraction of the wall-clock training time.

• The paper introduces a scalable architecture (Ape-X) that decouples data collection and learning, significantly accelerating deep RL training.
• It employs prioritized experience replay that focuses on high TD error transitions, improving sample efficiency and convergence speed.
• Distributed data generation with hundreds of actors enhances exploration, achieving state-of-the-art performance on both Atari and continuous tasks.

Distributed Prioritized Experience Replay: A Summary

The paper "Distributed Prioritized Experience Replay" introduces a scalable architecture for deep reinforcement learning that decouples the data-generating and learning components of the RL pipeline. The proposed framework achieves significant advancements in the training efficiency and performance of agents by leveraging a distributed setup.

Key Elements of the Proposed Framework

The architecture, named Ape-X, utilizes a distributed prioritized experience replay mechanism to enhance the learning process through the following key components:

1. Decoupling of Acting and Learning: Unlike traditional setups where the acting and learning phases are tightly coupled, Ape-X decouples these processes. Actors generate experience data by interacting with environment instances and store this data in a shared replay memory. Learners then sample from this memory to update the network.
2. Prioritized Experience Replay: The framework employs prioritized experience replay, focusing on transitions that provide the most significant learning opportunities, such as those with high temporal-difference (TD) errors. The prioritization enables the learners to make more efficient updates, leading to faster convergence.
3. Distributed Data Generation: Hundreds of CPU-based actors generate data concurrently, thereby providing a huge volume of diverse experiences. This massive parallel data generation ensures a broad exploration of the environment, mitigating local optima issues.
4. Periodic Parameter Update: The actors periodically receive updated network parameters from the learner, ensuring they operate using the most recent policy, which balances exploration and exploitation.

Empirical Evaluation and Results

The framework was empirically validated on both discrete and continuous control tasks, showing substantial improvements over existing methods.

Atari Benchmark

For the Atari suite, Ape-X DQN achieved state-of-the-art performance, surpassing multiple baselines such as DQN, Prioritized DQN, and Rainbow. The experimental setup employed 360 actors operating at a combined rate of approximately 50,000 frames per second. The learner, using a GPU, processed 19 batches per second, equivalent to about 9,700 transitions per second. The results indicated not only faster training but also higher final performance:

• Median Human-Normalized Score: The median human-normalized score was 434% for no-op starts and 358% for human starts, significantly outperforming Rainbow (223% and 153%, respectively).

Continuous Control Tasks

Applying Ape-X to Deep Deterministic Policy Gradient (DDPG) for continuous control tasks further validated the generality of the framework. On tasks like humanoid walking and running, Ape-X DPG demonstrated superior performance by effectively handling high-dimensional action spaces and producing substantial improvements over vanilla DDPG:

• Scalability: Performance consistently improved with an increase in the number of actors from 8 to 256, primarily due to the broader state space exploration and efficient learning through prioritized replay.

Discussion and Implications

Scalability and Efficiency

The approach scales efficiently with an increasing number of actors, as demonstrated by improved performance across various tasks. By harnessing large-scale compute resources, the framework effectively addresses exploration challenges and helps avoid overfitting—a common issue in deep reinforcement learning.

Exploration and Exploitation Balance

Ape-X introduces a heterogeneous exploration strategy by allocating different exploration parameters (ε-values) to various actors. This ensures diverse experience gathering while the prioritization mechanism inherently directs the learning process toward the most informative experiences.

Practical Applicability

The distributed nature of Ape-X makes it highly applicable in real-world scenarios that involve large-scale and diverse environment instances, such as robotic arm farms and self-driving cars. However, its reliance on extensive computational resources suggests it may be less suited for applications where data generation is expensive or infeasible.

Future Directions

Given the success of Ape-X, future research could explore:

1. Extending Prioritization Strategy: Adapting the prioritization mechanism to account for temporally extended sequences rather than single transitions could benefit algorithms leveraging trajectory-based learning methods, such as those implementing temporally extended actions.
2. Optimizing Resource Usage: While scaling up resources has proven beneficial, optimizing CPU, GPU, and network bandwidth usage further could yield more cost-effective and environmentally friendly implementations.

In conclusion, the Ape-X framework exemplifies a robust and scalable approach to deep reinforcement learning. By leveraging distributed systems for data generation and prioritization, the framework pushes the boundaries of what is achievable in both wall-clock training times and overall performance in high-dimensional environments.