Benchmarking Deep Reinforcement Learning for Continuous Control (1604.06778v3)

Abstract: Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at https://github.com/rllab/rllab in order to facilitate experimental reproducibility and to encourage adoption by other researchers.

• The paper introduces a new benchmark suite that features 31 continuous control tasks, providing a diverse testing ground for deep reinforcement learning algorithms.
• The paper systematically evaluates methods like TRPO, DDPG, and gradient-free approaches, highlighting their stability, convergence, and challenges in complex tasks.
• The paper offers an open-source tool for reproducibility, encouraging community collaboration to enhance DRL performance in high-dimensional, real-world control problems.

Benchmarking Deep Reinforcement Learning for Continuous Control

In the paper titled "Benchmarking Deep Reinforcement Learning for Continuous Control" by Yan Duan et al., the authors introduce a comprehensive benchmark suite designed to address challenges in quantifying progress in continuous control tasks within deep reinforcement learning (DRL). The motivation stems from the observation that existing benchmarks, like the Arcade Learning Environment (ALE), primarily cater to discrete action spaces, leaving a gap for continuous control applications which are more representative of real-world scenarios such as robotics.

Key Contributions

The paper's notable contributions include:

1. Benchmark Suite: A new suite of 31 continuous control tasks categorized into basic tasks, locomotion tasks, partially observable tasks, and hierarchical tasks. These tasks are implemented using state-of-the-art physics simulators, ensuring realistic dynamics.
2. Algorithm Implementations: Systematic evaluation of several DRL algorithms across the benchmark tasks, including REINFORCE, TNPG, TRPO, REPS, RWR, CEM, CMA-ES, and DDPG.
3. Open Source Tool: The release of the benchmark and reference implementations on GitHub to promote reproducibility and community engagement.

Task Categories

The benchmark covers a diverse set of tasks:

• Basic Tasks: Including Cart-Pole Balancing, Cart-Pole Swing Up, and Double Inverted Pendulum Balancing, which are low-dimensional and widely studied.
• Locomotion Tasks: These tasks involve higher-dimensional control problems, such as Swimmer, Hopper, and Full Humanoid, and present significant exploration challenges.
• Partially Observable Tasks: Variants of the basic tasks where observations are either limited or noisy, adding a layer of complexity by simulating real-world sensor imperfections.
• Hierarchical Tasks: Tasks that combine locomotion with higher-level objectives like Food Collection and Maze Navigation, designed to test the algorithms' ability to discover and exploit hierarchical structures.

Experimental Setup

For a fair comparison, the authors use consistent experimental setups across all tasks. Metrics like average return over training iterations and total undiscaptured reward are employed for performance evaluation. Hyperparameters for each algorithm are meticulously tuned using a grid search approach, with multiple random seed executions to ensure robustness.

Results and Discussion

The paper presents detailed performance results for the implemented algorithms. A few key observations include:

• TNPG and TRPO: These algorithms consistently outperformed others, leveraging their ability to constrain policy updates through trust regions, resulting in stable learning across various tasks.
• REINFORCE: While effective in simpler tasks, it often fell into local optima on more complex tasks due to its sensitive nature to step sizes.
• Gradient-Free Methods: CEM showed competitive performance on certain tasks, but both CEM and CMA-ES struggled with tasks involving higher-dimensional control policies.
• DDPG: Excelled in faster convergence for some locomotion tasks but demonstrated instability and sensitivity to reward scaling.
• Hierarchical and Partially Observable Tasks: All algorithms showed limited success, indicating the need for further research into methods that can exploit hierarchical structures effectively.

Implications and Future Directions

This paper sets a solid foundation for the systematic evaluation of DRL algorithms in continuous control. It underscores the importance of a diverse and challenging benchmark suite for uncovering algorithm strengths and weaknesses. Practically, the benchmark assists researchers in improving existing algorithms or developing new ones tailored for high-dimensional continuous action spaces. Theoretically, the findings provide insights into the efficacy and limitations of current DRL methods under various conditions.

Conclusion

The work of Duan et al. introduces an essential benchmarking tool for the DRL community, facilitating objective progress measurement in continuous control. The systematically gathered empirical evidence presented provides a baseline for future advancements and highlights the pressing need for algorithms capable of tackling hierarchical and partially observable challenges. The open-sourcing of this comprehensive benchmark suite further encourages collaborative improvement and widespread adoption among researchers focused on continuous control problems in DRL.