Evolving Reinforcement Learning Algorithms (2101.03958v6)

Abstract: We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behavior shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods.

• The paper proposes a meta-learning approach that evolves RL algorithms via computational graph search to automate algorithm design.
• It utilizes genetic programming to derive symbolic loss functions that often rediscover classical methods like temporal-difference learning.
• Evolved algorithms demonstrate improved sample efficiency and performance over baselines like DQN while reducing Q-value overestimation.

Evolving Reinforcement Learning Algorithms: Insights and Implications

The paper "Evolving Reinforcement Learning Algorithms" presents a novel approach to the meta-learning of reinforcement learning (RL) algorithms through the exploration of computational graph spaces to develop loss functions that optimize value-based, model-free RL agents. The method proposed aims to generate domain-agnostic algorithms capable of generalization across various environments, a significant advancement from the existing manually designed algorithms.

This research highlights the challenges inherent in designing reinforcement learning algorithms that can efficiently address diverse problems. Traditionally, the development of such algorithms requires substantial manual effort and is typically tailored for specific tasks or environments. The paper's approach involves conceptualizing the task of training an RL agent as a meta-learning problem, with an outer loop optimizing over a space of computational graphs to compute the objective function, and an inner loop executing updates using the derived loss function. The methodological innovation lies in using genetic programming to search for symbolic loss functions that are independent of any particular domain. This framework is designed to incorporate human knowledge by bootstrapping from existing algorithms like Deep Q-Networks (DQN), subsequently enhancing interpretability and performance.

The paper showcases that starting from scratch, the approach is capable of rediscovering the temporal-difference (TD) algorithm on simple classical control and gridworld tasks. When building upon DQN, the method demonstrates the development of new RL algorithms that surpass existing algorithms in performance across classical control tasks, gridworld environments, and even Atari games, despite the latter being dissimilar to training environments. Two evolved algorithms, referred to as $and$, display robust generalization capabilities and improved sample efficiency over other methods.

Numerical results underline the effectiveness of the evolved algorithms. These algorithms demonstrate substantial improvements over baseline algorithms, including DQN and Double-DQN (DDQN), particularly in terms of sample efficiency and final performance on validation and test environments. Notably, these algorithms introduce regularization to mitigate overestimation of Q-values, a known issue in value-based methods, aligning with recent advancements in RL like Conservative Q-learning (CQL) and Munchausen-DQN (M-DQN).

The implications of these findings are twofold. Practically, the approach offers a framework to automate the design of RL algorithms, potentially easing the manual burden placed on researchers and paving the way for the discovery of more efficient algorithms than current handcrafted methods. Theoretically, it provides insightful connections to existing RL innovations, suggesting an automated pathway to further explore and refine these innovations.

The potential future developments in AI from this research include expanding the approach to encompass a broader range of RL algorithms, such as actor-critic or policy gradient methods, and incorporating action sampling strategies into the search space. Furthermore, diversifying training environments could yield algorithms with even greater generalization capabilities.

In conclusion, the paper provides a comprehensive framework for evolving RL algorithms through meta-learning. It bridges the gap between machine-designed and human-designed algorithms, positioning itself as a significant step towards automating the discovery of robust, generalized reinforcement learning strategies.