Bigger, Better, Faster: Human-level Atari with human-level efficiency

Abstract: We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.

• The paper presents BBF, a value-based RL agent that scales its neural network architecture to attain human-level Atari performance with remarkable sample efficiency.
• The paper leverages a high replay ratio and periodic network resets to enhance learning dynamics and mitigate overfitting in sample-constrained environments.
• The paper integrates self-supervised learning with dynamic discounting and a receding update horizon to balance rapid convergence with improved asymptotic accuracy.

Overview of the Paper "Bigger, Better, Faster: Human-level Atari with Human-level Efficiency"

The paper introduces "Bigger, Better, Faster" (BBF), a reinforcement learning (RL) agent that excels in sample-efficient performance on the Atari 100K benchmark by achieving super-human scores. The BBF agent is a value-based model-free algorithm that achieves its remarkable efficiency and performance through a combination of scaling neural network architectures and deploying various enhancements that collectively improve training dynamics and RL efficacy.

Key Contributions and Findings

The researchers identify several critical components that enable the BBF agent to maintain unprecedented levels of efficiency and performance:

1. Network Scaling and Architecture: The agent employs a ResNet-based architecture derived from the Impala-CNN model and scales its width to 4 times the standard size. This architectural shift permits BBF to leverage larger networks, which improve its expressivity and learning efficiency, crucial in sample-constrained settings such as Atari 100K.
2. Replay Ratio and Network Resets: The paper highlights the significance of a higher replay ratio, set at 8, which enhances sample utilization by performing more learning updates per environmental sample. This is coupled with periodic network resets which mitigate overfitting and allow the neural network to maintain plasticity over longer periods of training.
3. Receding Update Horizon and Dynamic Discounting: An innovative approach involves starting with a larger $n$-step update horizon that narrows over time, alongside an increasing discount factor. These dynamic adjustments align with theoretical insights into balancing convergence speed with asymptotic accuracy in RL.
4. Use of Self-Supervision: The integration of a self-supervised objective, specifically derived from SPR (Self-Predictive Representations), enhances the learning process by providing a consistent training signal, independent of reward sparsity or stochasticity in the environment.
5. Computational Efficiency: Despite the increased network size, BBF maintains computational efficiency critical for broader applicability. It achieves this balance by foregoing NoisyNets and utilizing a smaller set of hyperparameters, enabled by the network architecture and self-supervision.

Implications and Future Directions

The implications of BBF's performance are manifold, particularly as RL systems venture into real-world applications where sample efficiency is paramount. The ability to maintain high learning rates with limited data showcases potential pathways for deploying RL in resource-constrained environments, possibly in domains like healthcare or robotics, where extensive interaction with the environment is costly or unfeasible.

Moreover, the insights into network scaling, the benefits of augmenting the replay ratio, and leveraging self-supervised methodologies suggest exciting avenues for further research. Future investigations might focus on applying these principles to continuous-action domains or varied benchmarks beyond ALE, potentially leveraging transfer learning via architectures similar to BBF.

Conclusion

The paper "Bigger, Better, Faster: Human-level Atari with Human-level Efficiency" underscores an emerging paradigm in RL research characterized by the strategic interplay between network scale, sample efficiency, and the incorporation of self-supervised learning elements. The findings invite further exploration into network architectures and learning paradigms that could extend BBF's principles to broader domains and applications.