Decoupling Value and Policy for Generalization in Reinforcement Learning
This paper introduces the Invariant Decoupled Advantage Actor-Critic (IDAAC) framework, aiming to address generalization challenges prevalent in deep reinforcement learning (RL). The traditional approach in RL often utilizes a shared representation for both the policy and value function, a practice that inadvertently exacerbates overfitting due to the asymmetrical information needs between estimating the value function and learning the optimal policy. The paper posits that distinct representations for the policy and value functions can alleviate this issue.
IDAAC comprises two primary contributions. First, it decouples policy optimization from value function estimation, employing separate neural networks for each. Second, the framework incorporates an auxiliary loss designed to enforce invariance against task-irrelevant environmental properties. The empirical evaluation substantiates IDAAC's capacity for robust generalization to unseen environments, achieving superior performance on the Procgen benchmark—a set of procedurally generated RL environments—and outperforming existing methods in DeepMind Control tasks that include distractors.
The authors illustrate the limitations of shared representations by highlighting the policy-value asymmetry, where a standard RL agent overfits to idiosyncratic features such as environment-specific backgrounds. This phenomenon is exemplified through the comparison of semantically identical, yet visually distinct, levels from the Procgen game Ninja. An agent trained to predict the value function is more likely to memorize correlations with non-causal features that do not necessarily support generalization.
By replacing the shared value-focused model with a specialized advantage prediction network, IDAAC enhances generalization. The advantage function, being a relative measure based on expected returns from particular actions, proves to be less susceptible to overfitting compared to absolute value predictions. Further, the incorporation of an adversarial loss constraining the policy representation ensures invariance to episodic-specific cues such as step count, further promoting generalization across procedurally distinct environments.
The experiment extensively contrasts IDAAC's performance against prominent RL methods, including PPO, UCB-DrAC, and PPG, across a range of RL environments. IDAAC consistently demonstrates superior generalization capability and test performance, notably featuring a smaller generalization gap compared to other models.
The paper substantiates the correlation between generalization ability and value loss, noting that agents trained on larger sets of levels exhibit increased value loss yet improved test performance. This observation further supports the assertion that decoupling policy and value optimization not only improves the accuracy of value predictions but also mitigates overfitting concerns.
The implications of this research are significant, contributing to both theoretical understanding and practical execution of RL in complex, diverse environments. By emphasizing the necessity of task-agnostic representations in RL, IDAAC sets a precedent for future explorations into auxiliary methods that enforce broader invariance and representational efficiency. As AI advancements continue, this work paves the way for more adaptable and generalized RL systems, fostering their deployment in real-world scenarios with varying dynamics and objectives.