[Paper Review] Terrain RL Simulator

Glen Berseth, Xue Bin Peng+1 more|arXiv (Cornell University)|Apr 17, 2018

Robotic Locomotion and Control8 references3 citations

TL;DR

This paper introduces TerrainRLSim, a library of 89 challenging continuous control environments with randomly generated terrain to increase task difficulty beyond standard benchmarks. It supports diverse agent morphologies, multiple control paradigms (torque, position, velocity, muscle-based), and terrain perception, enabling research in hierarchical reinforcement learning and continual learning.

ABSTRACT

We provide $89$ challenging simulation environments that range in difficulty. The difficulty of solving a task is linked not only to the number of dimensions in the action space but also to the size and shape of the distribution of configurations the agent experiences. Therefore, we are releasing a number of simulation environments that include randomly generated terrain. The library also provides simple mechanisms to create new environments with different agent morphologies and the option to modify the distribution of generated terrain. We believe using these and other more complex simulations will help push the field closer to creating human-level intelligence.

Motivation & Objective

• Address the lack of sufficiently challenging continuous control environments for deep reinforcement learning.
• Increase task difficulty by incorporating variable terrain geometry and distribution, affecting both action space complexity and perception demands.
• Enable the creation of new, customizable environments through parameterized terrain generation and flexible agent morphologies.
• Support advanced RL techniques such as hierarchical reinforcement learning and continual learning through novel environment designs.
• Promote the development of robust, generalizable policies by modeling realistic physical constraints and affordances in simulation.

Proposed method

• Design a physics-based simulation environment using the Bullet physics engine to support high-fidelity dynamics and real-time performance.
• Structure environments to expose local terrain state features (e.g., height, slope) as part of the observation vector, prioritized for processing by convolutional networks.
• Implement multiple action representation spaces: torque, desired velocity, desired position, and muscle activation for diverse control strategies.
• Introduce parameterized terrain generation to vary terrain complexity, shape, and distribution across episodes, increasing task diversity.
• Support reproducible training via configurable random seeds and modular environment construction for extensibility.
• Integrate existing motion capture data and extend environments to include multi-agent and multi-task learning scenarios (e.g., PLAiD).

Experimental results

Research questions

• RQ1 To what extent does terrain variability in simulation increase the difficulty of continuous control tasks beyond standard benchmarks?
• RQ2 Can hierarchical reinforcement learning effectively solve locomotion tasks in complex, randomly generated terrain environments?
• RQ3 How do different action representation spaces (e.g., muscle-based vs. torque) affect policy learning and generalization in dynamic terrain?
• RQ4 To what degree do realistic physical parameters (e.g., joint torque limits, friction, damping) improve the realism and challenge of RL environments?
• RQ5 Can the proposed environments support multi-task and continual learning in continuous control settings?

Key findings

• The library includes 89 distinct simulation environments with randomly generated terrain, many of which remain unsolved using standard DRL methods.
• Several environments, particularly those with complex terrain and dynamic obstacles, require hierarchical reinforcement learning to achieve successful policy learning.
• The integration of terrain state features into the observation vector enables effective use of convolutional networks for perception-based control.
• The inclusion of muscle-based control and multiple actuation models expands the range of applicable RL algorithms and enables more biologically plausible learning.
• The environments support high-fidelity, reproducible simulations with performance suitable for training and evaluation, thanks to the optimized Bullet physics engine.
• The library enables new research in continual and multi-task reinforcement learning, with dedicated environments like PLAiD supporting such scenarios in 3D continuous control.