Enter the Void - Planning to Seek Entropy When Reward is Scarce

Abstract: Model-based reinforcement learning (MBRL) offers an intuitive way to increase the sample efficiency of model-free RL methods by simultaneously training a world model that learns to predict the future. MBRL methods have progressed by largely prioritising the actor; optimising the world model learning has been neglected meanwhile. Improving the fidelity of the world model and reducing its time to convergence can yield significant downstream benefits, one of which is improving the ensuing performance of any actor it may train. We propose a novel approach that anticipates and actively seeks out high-entropy states using short-horizon latent predictions generated by the world model, offering a principled alternative to traditional curiosity-driven methods that chase once-novel states well after they were stumbled into. While many model predictive control (MPC) based methods offer similar alternatives, they typically lack commitment, synthesising multi step plans after every step. To mitigate this, we present a hierarchical planner that dynamically decides when to replan, planning horizon length, and the weighting between reward and entropy. While our method can theoretically be applied to any model that trains its own actors with solely model generated data, we have applied it to just Dreamer as a proof of concept. Our method finishes the Miniworld procedurally generated mazes 50% faster than base Dreamer at convergence and the policy trained in imagination converges in only 60% of the environment steps that base Dreamer needs.

An Analytical Overview of "Gaze Into the Abyss - Planning to Seek Entropy When Reward is Scarce"

The paper entitled "Gaze Into the Abyss - Planning to Seek Entropy When Reward is Scarce," authored by Ashish Sundar, Chunbo Luo, and Xiaoyang Wang, presents a compelling approach to enhancing model-based reinforcement learning (MBRL) by focusing on the strategic exploration of high-entropy states. The methodology introduces a novel paradigm that emphasizes the importance of improving the fidelity of the world model in MBRL frameworks, which is often overshadowed by the focus on actor optimization.

Core Contributions

The central thesis asserts that traditional curiosity-driven methods lack real-time adaptability, mainly due to their retrospective nature of rewarding the revisitation of previously discovered novel states. Anticipatory planning is proposed as an alternative, which leverages the world model's predictive capabilities to actively seek high-entropy states, thereby providing an exploratory framework that adapts effectively to stochastic and non-stationary environments.

Key contributions of the paper include:

1. Transition Uncertainty Utilization: By utilizing the world model's transition uncertainty to predict short-horizon state entropy, the authors propose a method to densify sparse rewards. This innovation provides a principled basis for accelerating model-based training as it refocuses the exploration on enhancing the world model's fidelity.

2. Hierarchical Planning Strategy: A reactive hierarchical planner is devised, which dynamically balances the exploration-exploitation trade-off by flexibly adjusting the planning horizon and the entropy-reward weighting. This strategy ensures a commitment to promising trajectories while allowing for adaptability through replanning options.

3. Adversarial Reformulation: The innovation extends to reformulating Dreamer's KL divergence minimization into an adversarial min-max objective. This formulation is designed to maximize the world model's information gain during learning, thereby directly optimizing its exploration strategy.

Experimental Validation

The implementation of the methodology in Dreamer demonstrates significant improvements in environment interaction efficiency. Specifically, the method concludes Miniworld's procedurally generated maze tasks 50% faster than the base Dreamer and requires only 60% of the environment steps to converge.

The paper's experiments utilize a procedurally generated 3D maze developed within the MiniWorld framework, where the challenge is compounded by partial observability and sparse rewards. The generalized performance across varying porosity levels underlines the robustness of the approach. The hierarchical planner, by dynamically adjusting parameters like planning probability and horizon, consistently outperforms not only Dreamer but also traditional model-free approaches such as PPO, especially in environments with increased complexity.

Implications and Future Directions

The implications of this work are twofold. Practically, this method provides a pathway for more efficient and adaptable RL systems in sparse-reward settings, which hold particular promise for real-world applications such as autonomous navigation and exploratory robotics. Theoretically, it reframes the role of exploration in MBRL by prioritizing the epistemic gain of the model, thus ensuring that the agent's behavior is tuned towards optimizing its understanding of the environment rather than merely maximizing immediate rewards.

Future research could explore integrating such exploration strategies with more advanced model architectures, such as Transformer-based state-space models, potentially enhancing their applicability and performance in even more complex and dynamic domains. Moreover, the potential incorporation of external semantic knowledge or LLMs could address the vulnerability of hidden epistemic uncertainties, further augmenting the robustness of the exploration process.

This paper represents a significant step toward refining the exploratory strategies employed in MBRL, promoting methods that not only enhance immediate task performance but also foster enduring improvements in the agent's world model comprehension and efficiency.