ProSpec RL: Plan Ahead, then Execute

TL;DR

ProSpec RL introduces a prospective planning approach in reinforcement learning by imagining future trajectories, improving decision-making, safety, and data efficiency, validated on DMControl benchmarks.

Contribution

The paper presents ProSpec RL, a novel method integrating future trajectory imagination and cycle consistency for safer, more efficient reinforcement learning.

Findings

Achieved significant performance improvements on DMControl benchmarks.

Enhanced safety by avoiding irreversible states through cycle consistency.

Improved data efficiency via virtual trajectory augmentation.

Abstract

Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental to human cognition. However, mainstream model-free Reinforcement Learning (RL) methods lack the ability to proactively envision future scenarios, plan, and guide strategies. These methods typically rely on trial and error to adjust policy functions, aiming to maximize cumulative rewards or long-term value, even if such high-reward decisions place the environment in extremely dangerous states. To address this, we propose the Prospective (ProSpec) RL method, which makes higher-value, lower-risk optimal decisions by imagining future n-stream trajectories. Specifically, ProSpec employs a dynamic model to predict future states (termed "imagined states") based on the current state and a series of sampled actions. Furthermore, we integrate the concept of Model Predictive Control and introduce a cycle consistency constraint that allows the agent to evaluate and select the optimal actions from these trajectories. Moreover, ProSpec employs cycle consistency to mitigate two fundamental issues in RL: augmenting state reversibility to avoid irreversible events (low risk) and augmenting actions to generate numerous virtual trajectories, thereby improving data efficiency. We validated the effectiveness of our method on the DMControl benchmarks, where our approach achieved significant performance improvements. Code will be open-sourced upon acceptance.