Evolving Reservoirs for Meta Reinforcement Learning (2312.06695v2)

Abstract: Animals often demonstrate a remarkable ability to adapt to their environments during their lifetime. They do so partly due to the evolution of morphological and neural structures. These structures capture features of environments shared between generations to bias and speed up lifetime learning. In this work, we propose a computational model for studying a mechanism that can enable such a process. We adopt a computational framework based on meta reinforcement learning as a model of the interplay between evolution and development. At the evolutionary scale, we evolve reservoirs, a family of recurrent neural networks that differ from conventional networks in that one optimizes not the synaptic weights, but hyperparameters controlling macro-level properties of the resulting network architecture. At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL). Within an RL agent, a reservoir encodes the environment state before providing it to an action policy. We evaluate our approach on several 2D and 3D simulated environments. Our results show that the evolution of reservoirs can improve the learning of diverse challenging tasks. We study in particular three hypotheses: the use of an architecture combining reservoirs and reinforcement learning could enable (1) solving tasks with partial observability, (2) generating oscillatory dynamics that facilitate the learning of locomotion tasks, and (3) facilitating the generalization of learned behaviors to new tasks unknown during the evolution phase.

• The paper proposes ER-MRL, integrating reservoir computing with meta-RL and evolutionary strategies to enhance learning adaptability.
• It shows that evolved reservoirs improve performance in partially observable tasks, locomotion dynamics, and generalization to novel environments.
• The study paves the way for biologically-inspired, efficient RL systems with practical implications in robotics and adaptive interfaces.

Evolving Reservoirs for Meta Reinforcement Learning: A Summary

This paper titled "Evolving Reservoirs for Meta Reinforcement Learning" explores the dynamic interplay between evolution and learning through the lens of computational models. The authors propose the novel architecture ER-MRL which integrates principles from Reservoir Computing (RC) and Meta Reinforcement Learning (Meta-RL) within an evolutionary computation framework.

Overview

The paper addresses how neural structures, evolved at an evolutionary scale, can improve the adaptability and learning capabilities of artificial agents within their developmental lifetime. The framework employs ER-MRL, which leverages the RC model to generate recurrent neural network structures called reservoirs, characterized by macro-level properties controlled by hyperparameters. These properties are meta-optimized using Evolutionary Algorithms (EAs), specifically the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), resulting in reservoirs that enhance task performance in the inner RL loop.

Results

The empirical evaluation of ER-MRL on various simulated environments highlights its capability to enhance learning in several key scenarios. The research primarily focuses on tasks exhibiting three core challenges:

1. Partial Observability: The evolved reservoirs demonstrate the ability to infer missing information, thus allowing agents to solve partially observable tasks effectively. This capacity is evidenced across several RL benchmarks like CartPole, where conventional RL approaches falter under reduced observability conditions.
2. Oscillatory Dynamics for Locomotion: The ER-MRL architecture facilitates the emergence of oscillatory patterns analogous to Central Pattern Generators (CPGs). These dynamics offer potential advantages in 3D locomotion tasks across varied agent morphologies.
3. Generalization to Unseen Tasks: The paper uncovers scenarios where evolved reservoirs enable agents to generalize learning to environments not encountered during the evolutionary phase. Specifically, reservoirs improved task completion on models with differing morphologies (e.g., HalfCheetah and Swimmer environments), suggesting that the reservoirs encode pertinent dynamics that generalize beyond the trained tasks.

Theoretical and Practical Implications

The integration of evolved reservoirs within ER-MRL contributes theoretically by aligning with neurobiological principles, notably the genomic bottleneck and functional reservoir-like structures in the brain. Practically, it introduces an adaptable method for enhancing RL applications' generalization and efficiency—particularly in contexts where task-specific adaptation is critical, such as robotics and adaptive user interfaces.

Speculation on Future Developments

This approach opens avenues for further exploration into larger, more varied pools of environments and morphologies, potentially cumulating in generalized reservoirs useful across wide tasks. Moreover, the exploration of incorporating more sophisticated Meta-RL techniques could refine the weighting initialization of RL policies, thereby reducing reliance on random initializations and further enhancing efficiency.

Conclusion

This work offers a compelling framework that merges fundamental ideas from several AI domains, paving the way for more biologically-aligned and computationally efficient learning systems. While the empirical results are promising, future research could explore optimizing reservoir configurations and broadening the scope of task environments to unlock the full potential of the ER-MRL framework.