Data-Efficient Reinforcement Learning with Self-Predictive Representations (2007.05929v4)

Abstract: While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interaction with the environment, learning from limited interaction remains a key challenge. We posit that an agent can learn more efficiently if we augment reward maximization with self-supervised objectives based on structure in its visual input and sequential interaction with the environment. Our method, Self-Predictive Representations(SPR), trains an agent to predict its own latent state representations multiple steps into the future. We compute target representations for future states using an encoder which is an exponential moving average of the agent's parameters and we make predictions using a learned transition model. On its own, this future prediction objective outperforms prior methods for sample-efficient deep RL from pixels. We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation. Our full self-supervised objective, which combines future prediction and data augmentation, achieves a median human-normalized score of 0.415 on Atari in a setting limited to 100k steps of environment interaction, which represents a 55% relative improvement over the previous state-of-the-art. Notably, even in this limited data regime, SPR exceeds expert human scores on 7 out of 26 games. The code associated with this work is available at https://github.com/mila-iqia/spr

• The paper introduces a novel SPR methodology that integrates self-supervised objectives with RL to enhance sample efficiency from visual observations.
• It employs a dual-network structure with online and target networks and uses data augmentation to ensure robust latent state predictions.
• Empirical results on the Atari 100k benchmark show a 55% improvement over previous methods, with SPR surpassing expert human performance on several games.

An Analysis of "Data-Efficient Reinforcement Learning with Self-Predictive Representations"

The paper "Data-Efficient Reinforcement Learning with Self-Predictive Representations" by Schwarzer et al. presents a novel methodology, referred to as Self-Predictive Representations (SPR), aimed at enhancing the sample efficiency of reinforcement learning (RL) from visual observations. The central premise of SPR is that by integrating self-supervised objectives with traditional RL frameworks, particularly in environments where interaction data is limited, agents can achieve superior performance while preserving computational practicality.

Overview of the SPR Approach

SPR integrates a self-supervised learning paradigm within the reinforcement learning framework to improve data efficiency. The paper builds on the intuition that encouraging state representations to be predictive of future states can enhance the effectiveness of learning from sparse interaction data. The methodology involves training an agent to predict its latent state representations multiple steps into the future by incorporating an auxiliary loss function derived from these predictions. This process employs a transition model operating entirely within the latent space, which avoids the computational overhead associated with reconstructing raw pixel representations.

A critical component of this framework is the division of the agent's neural network into two components: an online network and a target network, with the latter being an exponential moving average of the online network's parameters. The introduction of data augmentation to this setup further compels the agent's representations to remain consistent across multiple observational views, leveraging the natural structure in input data to bolster generalization and predictive power.

Empirical Evaluation

SPR's efficacy is substantiated through empirical evaluations on the Atari 100k benchmark—a notably challenging setting restricted to only 100,000 steps of environment interaction. This benchmark is particularly relevant as it approximates the time a human expert would have to learn these games, thus providing a fair comparison of data efficiency. SPR achieves a median human-normalized score of 0.415, marking a 55% improvement over the previous state-of-the-art. Significantly, SPR exceeds expert human performance on 7 out of the 26 games tested, despite the stringent data limitations.

Furthermore, the paper details a comprehensive ablation analysis examining the contributions of each component within SPR. Particularly, it underscores the critical role of using a separate target network and the predictive dynamics modeling as pivotal factors in the enhanced performance.

Implications and Future Directions

The introduction of SPR represents a meaningful stride in the domain of data-efficient RL, particularly pertinent to real-world applications where data acquisition is costly and interaction time is limited. By demonstrating significant performance gains without resorting to extensive environment interaction, the approach highlights the potential of integrating self-supervised learning techniques into existing RL frameworks.

The implications of this research could extend to several domains beyond gaming, including robotics and autonomous systems, where operational environments provide limited opportunities for trial and error. The utility of SPR's predictive framework may also inspire future research into leveraging large corpuses of unlabeled data, as is common in other areas of machine learning such as computer vision and natural language processing.

The paper also opens the door for further exploration into the integration of self-supervised learning with model-based planning systems. Future work could explore whether SPR's latent dynamics model could serve as a foundation for improved planning in model-based RL settings, potentially enhancing both sample efficiency and asymptotic performance.

In conclusion, the paper by Schwarzer et al. contributes valuable insights into improving RL efficiency through self-predictive state representations, paving the way for innovations in RL applications where data constraints are prevalent.