Evolving Curricula with Regret-Based Environment Design (2203.01302v3)

Abstract: It remains a significant challenge to train generally capable agents with reinforcement learning (RL). A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agent's capabilities. These methods benefit from their generality, with theoretical guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces. By contrast, evolutionary approaches seek to incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. In this paper we propose to harness the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing significant empirical gains in a diverse set of environments. An interactive version of the paper is available at accelagent.github.io.

• The paper introduces ACCEL, which leverages evolutionary mutations guided by regret signals to create adaptive curricula for RL agents.
• It demonstrates enhanced zero-shot transfer and superior performance on grid navigation and BipedalWalker tasks with lower computational cost.
• The study highlights the integration of minimax regret with evolutionary methods as a robust strategy for training versatile RL agents.

Insights on "Evolving Curricula with Regret-Based Environment Design"

The paper "Evolving Curricula with Regret-Based Environment Design" introduces Adversarially Compounding Complexity by Editing Levels (ACCEL), a method that leverages an intersection of evolutionary strategies with reinforcement learning (RL) to generate robust training curricula for agents. The premise of using curricula in reinforcement learning aims to enhance an agent's ability to generalize by continuously adjusting the complexity of tasks to match the agent's growing capabilities. This work stands out for integrating a regret-based objective — traditionally used to promote robustness at equilibrium — with evolutionary tactics to foster open-ended learning in RL agents.

The challenge of developing generally-capable RL agents necessitates training across diverse and progressively strenuous environments. Traditional methods often fell short in balancing complexity and generalization effectively. Where random generation lacked efficiency in discovering challenging levels in vast design spaces, evolutionary approaches succeeded at generating increasing complexity but struggled with resource intensiveness and domain specificity.

ACCEL outperformed existing methods by constantly generating and curating environment levels at the forefront of the agent's capabilities. The approach follows an evolutionary perspective: starting with simple levels and compounding complexity through mutations (edits) drawn from previously discovered high-regret levels. This strategy not only supports empirical improvements across different environments but also retains theoretical robustness in achieving minimax regret strategies.

Key Results

The empirical evaluation of ACCEL was conducted across several challenging domains, including a grid-based navigation task with partial observability and a continuous-control task in challenging terrain. Notably, ACCEL showed:

• Enhanced performance in zero-shot transfer tasks for navigation domains over prior methods, with significant improvements in labyrinth and maze tasks.
• High performance in BipedalWalker environments, approaching superior results over baseline methods like PLR and DR in terms of return rate and generalization.
• Achieved this success while using a fraction of the computational resources typically required by purely evolutionary approaches, like POET.

ACCEL's efficacy is grounded in its ability to maintain a dynamic curriculum, starting simple and gradually evolving. This compounding complexity mechanism ensures that the learning trajectory is aligned with the agent's current capabilities, thereby avoiding the pitfalls of getting stagnated at intermediate complexity levels.

Implications and Future Directions

The implications of the ACCEL framework extend broadly within the field of reinforcement learning, primarily in automated curricula generation. This approach can inform the development of RL systems that need to operate in increasingly diverse environments without explicit human intervention in designing the complexity of training tasks.

From a theoretical standpoint, ACCEL reaffirms the utility of integrating minimax regret in evolving curricula, ensuring that trained policies exhibit robust performance across diverse environmental challenges. However, by using evolutionary techniques to guide curriculum development in a resource-efficient manner, it paves the way towards training more generally capable AI agents.

Potential future research could explore enhancing the diversity of levels produced under ACCEL's framework, further leveraging advanced mutation strategies, or incorporating mechanisms to adaptively pursue regions in the environment design space that promise fruitful exploration. Moreover, although ACCEL demonstrated impressive results in 2D navigation and continuous-control domains, extending this framework effectively to 3D environments or real-world tasks remains an exciting and vital challenge.

In summary, "Evolving Curricula with Regret-Based Environment Design" presents a significant step forward in adaptive curriculum learning, combining theoretical robustness and practical effectiveness. The method harmonizes aspects of evolutionary creativity with strategic regret minimization, pushing the envelope on training RL agents toward broader and more adaptive functionalities.