DeepMind Control Suite (1801.00690v1)

Abstract: The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents. The tasks are written in Python and powered by the MuJoCo physics engine, making them easy to use and modify. We include benchmarks for several learning algorithms. The Control Suite is publicly available at https://www.github.com/deepmind/dm_control . A video summary of all tasks is available at http://youtu.be/rAai4QzcYbs .

• The paper introduces a comprehensive benchmark for continuous control tasks, enabling consistent evaluation of reinforcement learning algorithms.
• It standardizes tasks with a uniform reward structure and robust simulation via the efficient MuJoCo physics engine.
• Benchmark results reveal that advanced algorithms like D4PG outperform others, offering actionable insights for ongoing reinforcement learning research.

DeepMind Control Suite: A Comprehensive Benchmark for Continuous Control Tasks

The DeepMind Control Suite represents a robust set of benchmarks designed to evaluate and compare reinforcement learning (RL) algorithms, focusing exclusively on continuous control tasks. Powered by the MuJoCo physics engine, the suite leverages the strengths of Python to provide an extensible and user-friendly environment.

Overview

The paper introduces the DeepMind Control Suite as an essential tool for RL research, emphasizing high-quality, stable, and well-documented code. Unlike other benchmark sets like OpenAI Gym, the DeepMind Control Suite integrates continuous state, time, and action spaces in a uniform reward structure, allowing for interpretable learning curves and aggregated suite-wide performance measures.

The Design and Structure of the Control Suite

The Control Suite standardizes tasks through a systematic approach towards the state, action, dynamics, observation, reward, termination, and evaluation:

• State: Represented as a vector of real numbers, with spatial orientations as unit quaternions. Initialization avoids a single state to prevent memorized solutions.
• Action: Defined within a unit box, except for Linearly-quadratic regulator (LQR) domains.
• Dynamics: Evolving through discrete time steps, primarily using semi-implicit Euler integration.
• Observation: Ensures strong observability, implemented as a Python OrderedDict.
• Reward: Scaled to the unit interval $[0,1]$, facilitated by the tolerance() function.
• Termination and Discount: Infinite-horizon tasks, with evaluation using fixed-length episodes of 1000 steps.

MuJoCo Physics Engine

The MuJoCo physics engine is particularly noted for its performance in articulated low-to-medium degree-of-freedom models interacting with other bodies. The engine's efficiency is bolstered by the MJCF definition format and its reconfigurable computation pipeline, making it a popular choice for robotics and RL research.

Domains and Tasks

The Control Suite comprises a diverse set of domains, including:

• Classic control problems like the Pendulum and Cart-Pole.
• More complex systems such as the Cheetah, Walker, and Humanoid models.
• Procedurally generated tasks, e.g., multi-link Swimmers and Humanoid_CMU, based on CMU's motion capture data.

Each domain is meticulously designed to ensure stability and solvability through extensive testing with various learning agents.

Benchmarking Algorithms

The paper provides benchmarking results for several RL algorithms applied both to low-dimensional feature observations and raw-pixel inputs:

• A3C (Asynchronous Advantage Actor-Critic): Trained with 32 workers, prioritized stability through hyperparameter tuning.
• DDPG (Deep Deterministic Policy Gradient): Implemented in a single actor/learner setup with an emphasis on continuous action spaces.
• D4PG (Distributed Distributional DDPG): Extending DDPG with distributional value functions and parallel processing via the Ape-X architecture.

Results and Analysis

The results reveal that D4PG generally outperforms other algorithms across most tasks, showcasing superior performance in terms of robustness, data efficiency, and final performance. However, learning from raw-pixel data poses additional challenges, particularly in domains with complex visual states. Despite these challenges, D4PG variants with shared convolutional layers between actor and critic demonstrated promising results.

Implications and Future Work

The DeepMind Control Suite provides a valuable benchmarking tool that highlights the strengths and weaknesses of contemporary RL algorithms for continuous control tasks. The focus on a uniform reward structure and interpretable learning curves facilitates easier comparison across different algorithms.

In future developments, planned enhancements include the introduction of quadrupedal locomotion tasks, interactive visualizers, multi-threaded dynamics, and potentially a TensorFlow wrapper for MuJoCo. These expansions aim to support more complex and rich task sets, further pushing the boundaries of RL research.

Conclusion

The DeepMind Control Suite elevates the standard for benchmarking in reinforcement learning research, presenting a comprehensive and meticulously structured set of continuous control tasks. By offering a clear, interpretable framework and robust baselines, it serves as a vital resource for researchers aiming to advance the field of AI through more rigorous and reproducible experimentation.