ProSpec RL: Plan Ahead, then Execute

TL;DR

ProSpec RL addresses the gap in model-free reinforcement learning where agents lack proactive planning. It introduces a Flow-based Dynamics Model (FDM) with RealNVP and Orthogonal Weight Normalization to imagine multiple future trajectories and applies a Model Predictive Control–style decision mechanism with value consistency to select actions that maximize long-term return while minimizing risk. The approach also uses cycle consistency and action augmentation to improve state traceability and data efficiency, enabling a large number of virtual trajectories from imagined futures. Empirically, ProSpec yields strong improvements on DMControl benchmarks with limited interaction, outperforming several baselines and achieving near-maximal performance on multiple tasks, suggesting practical benefits for data-efficient planning in continuous control.

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.

Figures (2)

• Figure 1: ProSpec RL process. First, the encoder encodes the initial state $s_0$ provided by the environment into a latent state representation $z_0$. Upon receiving $z_0$, the agent performs $t$ step forward predictions from multiple perspectives ($k$) within ProSpec, resulting in $k$ predicted future states $\{\hat{z}_{t1},\cdots,\hat{z}_{tk}\}$. Then ProSpec chooses the optimal action $a^*_0$ to execute as $a^*_0 = \underset{a_0}{\text{arg max}}\left\{CQ_1, \cdots,CQ_k\right\}$, where $CQ_i = \sum^t_{j=0}\gamma^jQ(\hat{z}_j,\hat{a}_j)$ is the cumulative discounted return.
• Figure 2: Training Procedure of ProSpec. Here, $\mathcal{J}_\theta$ represents the reinforcement learning loss; $\mathcal{L}_{pred}$ denotes the prediction loss of the Flow-based Dynamics Model (FDM); $\mathcal{L}_c$ stands for the cycle consistency loss.