Introduces textual belief states and factorized GRPO to enforce strict latent state mediation in text-based world models, yielding preserved prediction accuracy with large gains in representation quality and rollout performance on TextWorld and ScienceWorld.
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Dream to Control: Learning Behaviors by Latent Imagination
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Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.
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- abstract Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual contro
- background Langugae-Conditoned MoCoGAN [29], U-Net [30], Latte [ 31], Wan [32], Sora 2 [ 33]. . . Embodied World Model SWIM [34], DreamDojo [ 35], RoboDreamer [36], RoboScape [37]. . . WM for VLA Imitation Learning Ctrl-World [38], RoboScape [37], DREMA [ 39] Reinforcement Learning Dreamer to Control [ 40] DreamerV2 [ 41], Dreamer 4 [ 42], RISE [ 43] DreamerV3 [44], DayDreamer [45], World-Env [46], RoboScape-R [47] WMPO [48], WoVR [49], VLA-RFT [50], RWML [51], MoDem-V2 [52] World-Gymnast [53], RWM-U [54],
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citing papers explorer
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Safety, Security, and Cognitive Risks in World Models
World models enable efficient AI planning but create risks from adversarial corruption, goal misgeneralization, and human bias, demonstrated via attacks that amplify errors and reduce rewards on models like RSSM and DreamerV3.