Meta-Reinforcement Learning of Structured Exploration Strategies (1802.07245v1)

Abstract: Exploration is a fundamental challenge in reinforcement learning (RL). Many of the current exploration methods for deep RL use task-agnostic objectives, such as information gain or bonuses based on state visitation. However, many practical applications of RL involve learning more than a single task, and prior tasks can be used to inform how exploration should be performed in new tasks. In this work, we explore how prior tasks can inform an agent about how to explore effectively in new situations. We introduce a novel gradient-based fast adaptation algorithm -- model agnostic exploration with structured noise (MAESN) -- to learn exploration strategies from prior experience. The prior experience is used both to initialize a policy and to acquire a latent exploration space that can inject structured stochasticity into a policy, producing exploration strategies that are informed by prior knowledge and are more effective than random action-space noise. We show that MAESN is more effective at learning exploration strategies when compared to prior meta-RL methods, RL without learned exploration strategies, and task-agnostic exploration methods. We evaluate our method on a variety of simulated tasks: locomotion with a wheeled robot, locomotion with a quadrupedal walker, and object manipulation.

• The paper introduces MAESN, a gradient-based fast adaptation algorithm that integrates knowledge from previous tasks to guide exploration.
• It employs a latent variable policy to inject temporally coherent structured noise, outperforming traditional meta-RL and task-agnostic methods.
• The study demonstrates that structured exploration enhances learning speed and adaptation in sparse reward and high-dimensional environments.

Meta-Reinforcement Learning of Structured Exploration Strategies

The research paper titled "Meta-Reinforcement Learning of Structured Exploration Strategies" addresses a critical challenge in the field of reinforcement learning (RL), specifically the difficulty of crafting effective exploration strategies in complex environments. Traditionally, exploration methods in RL focus on task-agnostic goals like maximizing state visitation or using entropy bonuses without leveraging the knowledge from previously encountered tasks. The authors propose an approach that integrates knowledge from related tasks to guide exploration in new contexts, aiming to improve the efficacy of the learning process.

The core contribution of the paper is the introduction of a gradient-based fast adaptation algorithm called "Model Agnostic Exploration with Structured Noise" (MAESN). This approach employs meta-learning to derive exploration strategies by utilizing prior experience. The algorithm incorporates a latent exploration space that injects structured stochasticity into the policy, thus creating exploration strategies that surpass the randomness of conventional approaches.

MAESN outperforms previous meta-reinforcement learning (meta-RL) methods and task-agnostic exploration techniques, as demonstrated through rigorous evaluations on simulated tasks such as robotic locomotion and manipulation. By adopting a policy gradient method that includes structured noise, MAESN effectively balances between exploration and exploitation, allowing for staggered yet coherent exploration that respects the continuity of tasks.

The paper methodically explicates the structure of MAESN. Employing latent variable policies allows the model to incorporate temporally coherent noise, sampled from a learned latent Gaussian distribution. This nuanced approach marks a departure from traditional policies that only introduce independent time-invariant noise, paving the path for more innovative exploration strategies.

By framing the exploration problem within a meta-learning framework, the authors demonstrate that MAESN's performance significantly leverages the information from structurally similar tasks, improving adaptation speeds during meta-test time. This is particularly relevant when faced with sparse reward settings in real-world applications, as seen in the experiments with robotic tasks.

In comparison to existing methods like RL^2 and MAML, the evaluations illustrate that MAESN's structured exploration mechanism affords it superior asymptotic performance and adaptation speed. This points to its potential applicability in practical settings where exploration costs are high or when dealing with high-dimensional sensory input spaces.

The implication of such research is substantial, suggesting that using an exploration strategy informed by past tasks can accelerate learning in new tasks—a notion with strong theoretical underpinnings and vast practical applications in AI and robotics. The latent space, conditioned on the meta-training process, enhances the RL agent's ability to perform efficient exploration while maintaining the adaptability akin to learning from scratch. Additionally, MAESN lays the groundwork for future explorations into combining meta-reinforcement learning with other exploration paradigms, thus opening avenues for integrating intrinsic motivation strategies within a meta-learning context.

In conclusion, the paper presents a compelling case for structured exploration in RL through meta-learning, demonstrating considerable improvements over traditional and early meta-RL strategies. Future research could expand MAESN's application to broader domains and explore integration with complementary methodologies for enhanced exploration efficacy.