Distributed Distributional Deterministic Policy Gradients (1804.08617v1)

Abstract: This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG. We also combine this technique with a number of additional, simple improvements such as the use of $N$-step returns and prioritized experience replay. Experimentally we examine the contribution of each of these individual components, and show how they interact, as well as their combined contributions. Our results show that across a wide variety of simple control tasks, difficult manipulation tasks, and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance.

• The paper introduces D4PG, which integrates distributional critics with deterministic policy gradients for enhanced continuous control.
• It employs distributed experience collection and N-step returns to expedite training and balance bias-variance trade-offs.
• Prioritized experience replay refines the learning signal, enabling state-of-the-art performance on high-dimensional control tasks.

Distributed Distributional Deterministic Policy Gradients

The paper "Distributed Distributional Deterministic Policy Gradients" introduces the Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithm. This algorithm is a synergy of various recent advances in reinforcement learning (RL), tailored for handling continuous control tasks.

Core Contributions

The primary contribution of this work is the adaptation and combination of the distributional perspective of reinforcement learning with deterministic policy gradients within a distributed framework. The resultant algorithm, D4PG, integrates several auxiliary techniques that enhance its performance significantly:

1. Distributional Critic Updates: The authors employ a distributional version of the critic in the actor-critic architecture. By modeling the value function as a distribution rather than a scalar, D4PG provides richer gradient signals for training the actor, leading to improved stability and performance.
2. Distributed Experience Collection: Utilizing multiple parallel actors to gather experience data significantly expedites the training process. This distribution of experience collection is executed in a manner reminiscent of methods like ApeX, reducing wall-clock time without compromising data quality.
3. N-step Returns: The algorithm incorporates N-step returns, which are known to provide a better bias-variance trade-off in temporal-difference learning, thereby facilitating learning in environments with delayed rewards.
4. Prioritized Experience Replay: D4PG further refines the learning signal by sampling experience based on the temporal-difference error, ensuring that more informative transitions have a higher likelihood of being replayed.

Experimental Evaluation

The empirical evaluation is robust, covering a diverse set of continuous control tasks. The results demonstrate that D4PG achieves state-of-the-art performance, outperforming several baselines, including canonical DDPG, especially on complex tasks with high-dimensional inputs and control requirements.

• The distributional critic yields evident advantages in stability and performance across tasks such as manipulation and locomotion.
• Distributional updates significantly benefit learning, as evidenced in harder tasks like Humanoid locomotion and manipulation.
• The combination of distributed actors and prioritized replay shows a substantial reduction in training time.

Theoretical and Practical Implications

The adoption of a distributional view in continuous control not only provides a theoretical framework for improving policy gradient methods but also suggests broader applicability. The results support the potential for distributional methods to enhance various RL algorithms beyond deterministic policy gradients.

From a practical standpoint, the reduction in wall-clock time due to distributed actors is invaluable for scaling RL algorithms to real-world applications, particularly in robotics and dynamic control systems where real-time decision-making is crucial.

Future Directions

Potential future work could explore the integration of D4PG with recent advancements in neural architecture and meta-learning methods. Further research could also address optimizing the parameterizations of distributional returns to enhance adaptability across diverse control tasks. Moreover, extending D4PG to multi-agent systems may reveal insights into distributed decision-making in complex, dynamic environments.

The D4PG algorithm represents a significant step forward in reinforcement learning for continuous control, showcasing how distributed and distributional methods can be effectively combined to achieve superior performance across challenging tasks.