reinformed-dreamer-asymmetric-world-model

name	reinformed-dreamer-asymmetric-world-model
description	Reinforced Dreamer methodology for asymmetric reinforcement learning using latent guidance to improve world model representations and behaviors in model-based RL.
paper_id	2607.26040

Reinforced Dreamer: Asymmetric World Model Training with Latent Guidance

Overview

This methodology addresses limitations in privileged information representations learned by asymmetric model-based reinforcement learning algorithms like Informed Dreamer. It proposes a novel asymmetric representation learning objective using latent guidance, resulting in the Reinforced Dreamer algorithm.

Key Contributions

• Identifies limitations in privileged information representations in existing asymmetric model-based RL approaches
• Proposes a novel asymmetric representation learning objective using latent guidance
• Demonstrates consistent improvement over standard Dreamer across multiple benchmarks
• Effective under both partial observability (with additional state info) and full observability (with refined state info)

Implementation Guidelines

1. World Model Architecture: Use a standard Dreamer-style world model with observation encoder, transition model, and reward predictor
2. Asymmetric Learning Setup: Provide additional supervision beyond rewards during training:
   • Under partial observability: use additional state information
   • Under full observability: use more refined state information
3. Latent Guidance Objective: Implement the proposed latent guidance objective to improve privileged information representations
4. Training Protocol: Train the asymmetric world model using the latent guidance objective alongside standard RL objectives

Use Cases

• Model-based reinforcement learning with partial observability
• Scenarios where additional state information is available during training but not deployment
• Applications requiring improved representation learning in world models
• Benchmark environments where standard Dreamer shows inconsistent performance

Activation Keywords

reinforced dreamer, asymmetric reinforcement learning, latent guidance, world model, model-based RL, privileged information

References

• arXiv:2607.26040 [cs.LG]
• Original Dreamer paper
• Informed Dreamer methodology