Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning (1706.00387v1)

Abstract: Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques. On the other hand, on-policy algorithms are often more stable and easier to use. This paper examines, both theoretically and empirically, approaches to merging on- and off-policy updates for deep reinforcement learning. Theoretical results show that off-policy updates with a value function estimator can be interpolated with on-policy policy gradient updates whilst still satisfying performance bounds. Our analysis uses control variate methods to produce a family of policy gradient algorithms, with several recently proposed algorithms being special cases of this family. We then provide an empirical comparison of these techniques with the remaining algorithmic details fixed, and show how different mixing of off-policy gradient estimates with on-policy samples contribute to improvements in empirical performance. The final algorithm provides a generalization and unification of existing deep policy gradient techniques, has theoretical guarantees on the bias introduced by off-policy updates, and improves on the state-of-the-art model-free deep RL methods on a number of OpenAI Gym continuous control benchmarks.

• The paper introduces the Interpolated Policy Gradient, a novel framework that fuses on-policy likelihood ratio gradients with off-policy deterministic estimates to balance bias and variance.
• It establishes theoretical performance bounds and shows that interpolating between stable on-policy and efficient off-policy techniques can optimize reinforcement learning.
• Empirical evaluations on MuJoCo environments demonstrate that the approach outperforms state-of-the-art algorithms while maintaining robust convergence.

Interpolated Policy Gradient: A Unified Approach to Reinforcement Learning

The paper "Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning" presents a novel approach in the domain of reinforcement learning (RL) by integrating on-policy and off-policy gradient estimation. This research bridges the gap between two popular methodologies in RL—on-policy and off-policy learning—by introducing a flexible family of policy gradient methods referred to as Interpolated Policy Gradient (IPG).

The paper begins by acknowledging the inherent trade-offs between on-policy and off-policy learning techniques. On-policy methods, despite their stability and ease of implementation, are often seen as data inefficient since they rely solely on current policy distributions for updates. Off-policy approaches, in contrast, exemplified by Q-learning, are praised for their sample efficiency as they can utilize previously collected data but suffer from potential instability due to the introduction of bias.

Theoretical Foundations and Methodology

IPG is formulated using control variates to merge on-policy likelihood ratio gradients and off-policy deterministic gradient estimates. This integration is structured within a parameterized framework allowing interpolation between the two types of learning. The paper identifies recent algorithms, such as TRPO and Q-Prop, as special cases under this unified framework, showcasing IPG's generality. The paper also provides a theoretical evaluation of the bias introduced by off-policy updates, offering assurances that such biases can be bounded.

A key contribution is the derivation of theoretical performance bounds, ensuring that the proposed method maintains a degree of reliability and convergence. The interpolated gradients are structured to maintain high performance across a range of agent-environment interactions by controlling the balance between bias (introduced by off-policy learning) and variance (a characteristic of on-policy learning).

Empirical Evaluation

Empirical results from experiments conducted on OpenAI Gym's MuJoCo environments indicate that IPG consistently outperforms state-of-the-art methods, including variations of actor-critic and purely gradient-based techniques. Crucially, the paper finds that the optimal performance often occurs mid-spectrum between on-policy and off-policy extremes, highlighting the efficacy of a mixed approach.

In scenarios where the variance reductions from off-policy samples are detrimental due to high bias (as seen with DDPG's heuristic exploration), IPG with bounded KL divergence maintains algorithmic stability. This emphasizes the advantage of IPG, which is able to utilize off-policy data without compromising convergence properties.

Implications and Future Directions

The introduction of IPG presents significant theoretical and practical implications. Theoretically, this work provides a framework for analyzing RL algorithms through the lens of interpolation between learning paradigms, potentially guiding future theoretical advancements. Practically, it offers a new tool for practitioners to harness the stability of on-policy methods alongside the sample efficiency of off-policy methods, therefore enabling more robust and efficient learning processes in complex environments.

Future research might explore extensions of IPG tailored for more complex policy structures or environments, such as those requiring multi-agent coordination or those with high-dimensional state-action spaces. Moreover, analysis of the long-term stability and adaptability of IPG under varying exploration strategies could yield fascinating insights, particularly in domains with uncertain and evolving dynamics.

In summary, the IPG approach provides a significant step toward more adaptive and efficient RL algorithms, paving the way for enhanced learning capabilities across a spectrum of applications. This integration not only advances theoretical understanding but also offers a practical benefit to leveraging varied learning paradigms in an increasingly data-driven landscape.