Assessing Generalization in Deep Reinforcement Learning (1810.12282v2)

Abstract: Deep reinforcement learning (RL) has achieved breakthrough results on many tasks, but agents often fail to generalize beyond the environment they were trained in. As a result, deep RL algorithms that promote generalization are receiving increasing attention. However, works in this area use a wide variety of tasks and experimental setups for evaluation. The literature lacks a controlled assessment of the merits of different generalization schemes. Our aim is to catalyze community-wide progress on generalization in deep RL. To this end, we present a benchmark and experimental protocol, and conduct a systematic empirical study. Our framework contains a diverse set of environments, our methodology covers both in-distribution and out-of-distribution generalization, and our evaluation includes deep RL algorithms that specifically tackle generalization. Our key finding is that `vanilla' deep RL algorithms generalize better than specialized schemes that were proposed specifically to tackle generalization.

• The paper introduces a new benchmark and protocol to rigorously assess deep RL agents' ability to generalize across diverse environments.
• It compares standard algorithms like A2C and PPO with specialized methods, revealing that vanilla RL often outperforms more complex approaches in generalization.
• Empirical results indicate agents interpolate well within familiar settings, yet extrapolation to extreme conditions remains a significant challenge.

Overview of "Assessing Generalization in Deep Reinforcement Learning"

The paper "Assessing Generalization in Deep Reinforcement Learning" investigates a pressing challenge in the reinforcement learning (RL) domain: the generalization capability of learned policies across varied environments. Authored by Packer et al., the paper acknowledges the foundational advancements deep RL has brought but underscores a critical limitation—agents often exhibit restricted generalization, optimizing within known environments yet faltering when confronted with new ones.

The paper addresses this gap by proposing a controlled and systematic approach to evaluate generalization. It introduces three core environment versions—Deterministic (D), Random (R), and Extreme (E)—designed to test interpolation within known environments and extrapolation beyond them. The differentiation among these versions facilitates a comprehensive understanding of how RL algorithms behave when encountering parameter variations in environmental dynamics.

Key Contributions

1. Benchmark and Experimental Protocol: The authors present a new benchmark and experimental protocol intended to serve as a standard for evaluating RL generalization capabilities. The framework incorporates a diverse set of environments including both classic control problems and advanced robotic simulations, enhancing the scope and applicability of the results.
2. Evaluation of Algorithms: The paper evaluates prominent RL algorithms—Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO)—alongside specialized algorithms like EPOpt and RL$^2$ focusing on generalization. These algorithms were scrutinized across various architectures, offering insights into how different strategies and configurations impact generalization.
3. Empirical Findings:
   • Intriguingly, the paper finds that "vanilla" deep RL algorithms such as A2C and PPO generally exhibit better generalization properties compared to their specialized counterparts when trained on stochastic environments.
   • EPOpt demonstrates improved generalization in specific cases, particularly within continuous action spaces, highlighting architecture-specific nuances.
   • RL$^2$, challenging due to training inefficiencies, showed limited success, suggesting a need for more sophisticated policy architectures.

Numerical Results and Implications

The empirical evaluation reveals significant variance in the generalization performance of RL algorithms contingent upon environmental stochasticity during training. Notably, algorithms trained on variable environments tend to interpolate more effectively, but extrapolation remains a formidable challenge. The results prompt a re-evaluation of prevailing assumptions in RL research, encouraging a shift towards simpler yet more robust training schemas as a viable pathway for developing generalized intelligence.

Theoretical and Practical Implications

The findings emphasize the importance of designing RL agents capable of operating in diverse and unforeseen environments. From a theoretical perspective, the paper challenges the community to refine understanding of how environmental characteristics interact with agent architectures to foster generalized learning. Practically, the benchmarks and protocol established offer a reproducible foundation for designing agents extensible beyond specific tasks, crucial for real-world applications where adaptability is paramount.

Future Directions

While the paper thoroughly assesses existing algorithms, several promising research avenues emerge:

• Investigating hybrid models that blend the strengths of both vanilla and specialized algorithms could yield more adaptable RL agents.
• Exploring model-based RL approaches may prove beneficial, given their inherent data efficiency and focus on learning system dynamics, potentially offering superior generalization.
• Further research into developing advanced policy architectures incorporating concepts like the Temporal Convolution Networks might alleviate some challenges observed with recurrent architectures.

In conclusion, the paper makes a substantial contribution by highlighting the limitations in current deep RL algorithms with respect to generalization, providing a solid groundwork for future explorations aimed at building more flexible and adaptive RL systems.