Improving Sample Efficiency in Model-Free Reinforcement Learning from Images (1910.01741v3)

Abstract: Training an agent to solve control tasks directly from high-dimensional images with model-free reinforcement learning (RL) has proven difficult. A promising approach is to learn a latent representation together with the control policy. However, fitting a high-capacity encoder using a scarce reward signal is sample inefficient and leads to poor performance. Prior work has shown that auxiliary losses, such as image reconstruction, can aid efficient representation learning. However, incorporating reconstruction loss into an off-policy learning algorithm often leads to training instability. We explore the underlying reasons and identify variational autoencoders, used by previous investigations, as the cause of the divergence. Following these findings, we propose effective techniques to improve training stability. This results in a simple approach capable of matching state-of-the-art model-free and model-based algorithms on MuJoCo control tasks. Furthermore, our approach demonstrates robustness to observational noise, surpassing existing approaches in this setting. Code, results, and videos are anonymously available at https://sites.google.com/view/sac-ae/home.

• The paper demonstrates that incorporating an image reconstruction loss is key to learning stable latent representations for improved sample efficiency.
• The paper introduces an end-to-end off-policy SAC+AE algorithm that integrates an auxiliary autoencoder to stabilize and enhance representation learning.
• The paper shows through DeepMind Control Suite benchmarks that SAC+AE rivals complex model-based methods while maintaining a simpler implementation.

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

The paper "Improving Sample Efficiency in Model-Free Reinforcement Learning from Images" addresses a significant challenge in reinforcement learning (RL): efficiently training agents to solve control tasks using high-dimensional image inputs. Traditional model-free RL approaches struggle when directly dealing with pixel data due to their poor sample efficiency. This paper's core contribution lies in its proposal for improving sample efficiency by integrating relevant feature extraction and employing off-policy algorithms, foremost among them the SAC+AE method, which uses an auxiliary autoencoder for more efficient representation learning.

Key Contributions

1. Necessity of Image Reconstruction Loss: The paper identifies that enforcing an image reconstruction loss is crucial to learn stable and effective latent representations from pixel inputs. This enables RL agents to capture complex features that are essential for task performance without relying on an extensive timestep reach, making the process more data-efficient.
2. End-to-End Training with Auxiliary Decoder: The authors propose an end-to-end off-policy actor-critic algorithm integrated with an auxiliary decoder network. This decoder becomes integral to feature extraction during policy training by reconstructing pixel inputs, which was a stabilization challenge in prior attempts at such integration.
3. Benchmarking and Comparisons: The paper provides empirical evidence showcasing that their proposed SAC+AE algorithm, when evaluated across several challenging tasks from the DeepMind Control Suite, performs comparably to more complex model-based methods like PlaNet and SLAC while maintaining simplicity. This positions their approach as a feasible alternative, especially when the complexity of model-based solutions or the construction of world models is undesirable.
4. Representation Learning: Through careful experimentation, the authors demonstrate that the learned latent state representation retains sufficient information to decode almost perfectly the environment's internal state, suggesting its expansive representational capacity.

Implications and Future Directions

The findings of this research carry practical implications for scenarios where quick and efficient learning from visual inputs is essential, such as in robotics or autonomous driving under computational constraints. By narrowing the gap between model-free and model-based methods in terms of sample efficiency, the method simplifies model training pipelines without sacrificing performance.

Future research can build upon these findings by exploring how incorporating other forms of auxiliary tasks or differing architectures might further enhance representation learning. Moreover, aligning these ideas with advancements in unsupervised and semi-supervised learning domains could augment the scalability and robustness of deployed RL systems.

Conclusion

Overall, this research strategically addresses the inefficiencies in model-free RL from image-based inputs by judiciously combining auxiliary reconstruction losses and off-policy strategies. It illuminates a promising path toward more practical, scalable, and efficient application of RL in pixel-rich environments while remaining straightforward in its implementation. The availability of their open-source code further encourages community engagement, potentially leading to broader validation and iteration of these ideas.