PPO Dash: Improving Generalization in Deep Reinforcement Learning (1907.06704v3)

Abstract: Deep reinforcement learning is prone to overfitting, and traditional benchmarks such as Atari 2600 benchmark can exacerbate this problem. The Obstacle Tower Challenge addresses this by using randomized environments and separate seeds for training, validation, and test runs. This paper examines various improvements and best practices to the PPO algorithm using the Obstacle Tower Challenge to empirically study their impact with regards to generalization. Our experiments show that the combination provides state-of-the-art performance on the Obstacle Tower Challenge.

• The paper introduces PPO-Dash, a suite of optimizations including action space and frame stack reductions along with recurrent memory to boost DRL performance.
• It demonstrates significant improvements in the Obstacle Tower Challenge, achieving higher floors completed than standard PPO methods.
• The study emphasizes the need for extended training to further elucidate the interplay of PPO-Dash components for robust generalization.

A Critical Analysis of PPO Dash: Enhancing Generalization in Deep Reinforcement Learning

The paper under review, authored by Joe Booth from Vidya Gamer, LLC, explores enhancements to the Proximal Policy Optimization (PPO) algorithm to improve its generalization capabilities in the context of Deep Reinforcement Learning (DRL). The research specifically addresses the limitations of traditional benchmarks such as Atari 2600 concerning overfitting, and leverages the Obstacle Tower Challenge as a more robust environment for testing generalization by incorporating randomized levels and novel visual themes. This structured approach aims at evaluating how elements of an improved PPO strategy, termed PPO-Dash, can yield state-of-the-art performance in these complex environments.

Problem Context and Motivation

Generalization challenges within DRL are an area of ongoing concern, particularly when traditional benchmarks fail to delineate performance effectively due to overfitting to specific tasks or environments. The Obstacle Tower Challenge was conceptualized to address this gap by employing randomly generated environments combined with withheld test seeds allowing for a comprehensive evaluation of an algorithm's ability to generalize. Notably, while PPO has shown efficacy in large-scale applications such as OpenAI Five, it has not performed optimally under high generalization demand settings within the Obstacle Tower framework, as evidenced by previously observed subpar results compared to algorithms like Rainbow.

Implementation of PPO-Dash

PPO-Dash is a suite of optimizations intended to enhance PPO's performance. The modifications span action and observation space reductions and leverage large-scale hyperparameters tuned for 3D sparse reward scenarios akin to the Obstacle Tower Environment. Key features of the PPO-Dash implementation include:

• Action Space Reduction: This significantly narrows the set of possible actions during training, which, for instance, reduced an initial action set of 54 to just 8 effective actions without sacrificing the agent's capacity to advance through game levels.
• Frame Stack Reduction: The research revisits the necessity of employing multiple frames as input, a holdover from older Atari settings, reducing reliance on such historical frames.
• Utilization of Recurrent Memory: Incorporation of recurrent network architectures to capitalize on temporal dependencies in tasks.

Notably, these modifications were informed by analogous practices in environments subjected to sparse rewards and a reliance on visually encoded state information via vector observations.

Experimental Insights and Comparative Performance

When tested against baseline implementations, PPO-Dash demonstrated substantial improvements, notably achieving second place in the Obstacle Tower Challenge's official standings for round one. The detailed results underscore a significant leap in floors completed compared to standard PPO implementations, particularly when recurrent memory and refined action sets were utilized.

Interestingly, the research also explores how individual components of PPO-Dash contribute to performance gains. While reduced action space consistently enhanced performance, other elements, when isolated, did not manifest substantial improvements within the experiment's limited training step range. This outcome alludes to potential dependencies or reinforcement interactions within PPO-Dash components that merit further investigation over extended iterations.

Implications and Future Directions

The implications of PPO-Dash on DRL extend to broad domains that require robust generalization under variable and complex conditions. This is particularly relevant in fields of robotics, autonomous vehicle training, and dynamic environment simulations. However, the inconclusive nature of element-wise impact suggests more extensive training iterations are imperative for elucidating the interaction between PPO-Dash components better.

Moreover, the paper posits that further exploration into how these best practices might translate to other reinforcement learning algorithms such as Rainbow could unlock even more generalized solutions, potentially leading to breakthroughs in environments characterized by a high degree of stochasticity and sparse feedback.

Conclusion

PPO-Dash represents a methodological advance in tuning policy optimization algorithms towards enhanced generalization. While the paper provides thoughtful insights into improving PPO's performance under randomized settings, it simultaneously highlights the inherent complexity and interplay of algorithm components, advocating for extended research and experimental rigor in future explorations. As the results indicate, transformative changes within reinforcement learning could hinge on these exploratory forays, fostering a more profound understanding of algorithmic generalization dynamics.