First return, then explore (2004.12919v6)

Abstract: The promise of reinforcement learning is to solve complex sequential decision problems autonomously by specifying a high-level reward function only. However, reinforcement learning algorithms struggle when, as is often the case, simple and intuitive rewards provide sparse and deceptive feedback. Avoiding these pitfalls requires thoroughly exploring the environment, but creating algorithms that can do so remains one of the central challenges of the field. We hypothesise that the main impediment to effective exploration originates from algorithms forgetting how to reach previously visited states ("detachment") and from failing to first return to a state before exploring from it ("derailment"). We introduce Go-Explore, a family of algorithms that addresses these two challenges directly through the simple principles of explicitly remembering promising states and first returning to such states before intentionally exploring. Go-Explore solves all heretofore unsolved Atari games and surpasses the state of the art on all hard-exploration games, with orders of magnitude improvements on the grand challenges Montezuma's Revenge and Pitfall. We also demonstrate the practical potential of Go-Explore on a sparse-reward pick-and-place robotics task. Additionally, we show that adding a goal-conditioned policy can further improve Go-Explore's exploration efficiency and enable it to handle stochasticity throughout training. The substantial performance gains from Go-Explore suggest that the simple principles of remembering states, returning to them, and exploring from them are a powerful and general approach to exploration, an insight that may prove critical to the creation of truly intelligent learning agents.

An Expert Analytical Overview of "First Return, Then Explore"

The paper "First Return, Then Explore" by Ecoffet et al. introduces a novel family of reinforcement learning (RL) algorithms known as Go-Explore. This research targets advancements in solving complex sequential decision problems characterized by sparse and deceptive reward feedback. Sparse rewards occur when achieving the final goal provides limited incremental feedback, while deceptive rewards may mislead the learning process towards local optima rather than true optimal solutions.

The authors explicitly identify two primary challenges in current exploration strategies that lead to inefficient learning: detachment and derailment. Detachment refers to the loss of ability or motivation by an algorithm to revisit promising states previously encountered, while derailment occurs when exploration actions prevent the algorithm from reliably reaching these states due to stochastic intervention or policy entropy.

To directly address these issues, Go-Explore leverages a systematic methodology comprising two fundamental components: a memory archive to stored promising states and a bifurcated action strategy—first, returning to remembered states, and then, decisively exploring new actions from those states. This approach distinctively separates exploration from exploitation, ensuring that the intrinsic challenge of returning to complex states is effectively managed, facilitating deeper environmental exploration than previously achievable with traditional RL methods.

The Go-Explore methodology has demonstrated outstanding proficiency in solving and surpassing state-of-the-art benchmarks within the Atari 2600 suite, a classic RL benchmark characterized by a diverse spectrum of exploration difficulties. Specifically, Go-Explore achieves superhuman performance and the resolution of intrinsically hard-exploration games such as Montezuma’s Revenge and Pitfall, offering orders of magnitude performance enhancement over predecessor algorithms. For example, the Go-Explore algorithm achieved a score of 43,791 on Montezuma's Revenge, significantly exceeding the previous best score of 11,618, illustrating the efficacy of this approach in overcoming the notorious challenge of sparse rewards.

Furthermore, Go-Explore's extensibility is demonstrated with its successful application in robotics tasks, where it tackles tasks with extreme reward sparsity, such as a robotic pick-and-place task. This versatility reflects Go-Explore's potential for broader applications in real-world tasks where exploration is a critical challenge.

The introduction of a goal-conditioned policy variant within Go-Explore is another notable enhancement, allowing adaptation to stochastic environments, ultimately leading to the mitigation of trajectory brittleness—a common issue when transitioning learned policies from deterministic to stochastic environments. This policy-based exploration utilizes space-filling curves in high-dimensional state spaces, optimizing the efficiency and robustness of exploration.

The implications of these findings are multi-fold. Practically, Go-Explore holds promise for applications far beyond the Atari and simulated robotics tasks evaluated, having potential utility in complex domains such as neural architecture search, language processing, and autonomous vehicle navigation—fields where state-spanning exploration under sparse or deceptive rewards has historically presented substantial hurdles. Theoretically, these results underscore the role of modular design principles in AI exploration strategies, advocating a more architectural rather than incremental approach to yet unattained learning objectives.

Future research directions include optimizations for real-time applications, adaptive memory management, and scalability across differing scale environments. The paper also hints at integrating learned representations and states obtained from simulations into real-world physics-based tasks, highlighting the potential of Go-Explore to catalyze progress towards generalized intelligent agents capable of solving tasks across a wide array of domains.