Open-World Reinforcement Learning over Long Short-Term Imagination

LS-Imagine: Defeating Short-Sightedness in Open-World Reinforcement Learning

Open-World Reinforcement Learning over Long Short-Term Imagination

2024-01-01

Jiajian Li, Qi Wang, Yunbo Wang, Xin Jin, Yang Li, Wenjun Zeng, Xiaokang Yang

Summary

Problem

Method

Results

Takeaways

Abstract

LS-Imagine is a novel Model-Based Reinforcement Learning (MBRL) framework designed for open-world visual control tasks like Minecraft. It achieves state-of-the-art success rates on MineDojo tasks by introducing "jumpy" state transitions and a long short-term world model that extends the agent's imagination horizon.

TL;DR

Visual Reinforcement Learning (RL) agents often struggle in open-world environments like Minecraft because their "imagination" is limited to the immediate future. LS-Imagine solves this by allowing agents to "jump" through time in their latent world models. By combining a Long Short-Term World Model with Affordance Maps, it reaches distant goals faster and more reliably, significantly outperforming baselines like DreamerV3 and VPT in the MineDojo benchmark.

The "Short-Sighted" Agent Problem

Modern Model-Based RL (MBRL) agents, such as DreamerV3, learn by simulating future trajectories in a latent space. While powerful, these simulations are usually restricted to short snippets (e.g., 15 steps). In an open-world setting where a tree might be hundreds of steps away, a 15-step lookahead is practically blind.

The core challenge is exploration efficiency. Without a way to bridge the gap between "here" and "there," agents wander aimlessly in the vast state space of raw pixels, failing to encounter the sparse rewards necessary for learning.

Methodology: High-Speed Jumps in Latent Space

The researchers introduced Long Short-Term Imagination (LS-Imagine), which fundamentally changes how an agent "dreams."

1. The Long Short-Term World Model

Unlike standard models that only predict $s_{t + 1}$ from $s_{t}$ , LS-Imagine features two branches:

Short-term branch: Predicts the immediate next state.
Long-term branch: Simulates "jumpy" transitions to a pivotal future state where a goal object is closer.

2. Affordance Maps via "Virtual Exploration"

To know where to jump, the agent needs a spatial prior. The authors developed a method to generate Affordance Maps by:

Scanning the current image with a sliding window.
"Zooming in" on these windows to simulate the agent moving closer.
Using a pretrained MineCLIP model to score the relevance of these "zoomed" clips to the text instruction (e.g., "cut a tree").

Model Architecture Figure 1: The general framework of LS-Imagine, enabling policy optimization in a joint space of short and long-term imaginations.

3. Jumpy State Transitions

When the affordance map indicates a high-value target is in view (measured by Kurtosis), a "jumping flag" $j_{t}$ is triggered. The model then predicts:

The post-jump state (what it looks like near the target).
The interval $Δ t$ (how many real steps it takes to get there).
The cumulative reward $G_{t}$ expected during that jump.

Results: Efficiency in Action

LS-Imagine was tested on five challenging MineDojo tasks. The results show a dramatic improvement in both success rates and efficiency.

Success Rate Comparison Figure 4: Performance comparison. LS-Imagine (red) reaches high success rates much faster than DreamerV3 (blue).

Sparse Target Advantage: In tasks like "Harvest log," LS-Imagine achieved an 80.6% success rate, whereas the previous SOTA, DreamerV3, hit only 53.3%.
Reduced Path Length: The agent doesn't just succeed more often; it succeeds faster. By "imagining" the long-term value, the learned policy is more direct, reducing the steps per episode by roughly 30%.

Critical Insight: Series vs. Parallel Imagination

A fascinating finding in the paper is the comparison between Series and Parallel imagination pathways.

Parallel: Jumping to a new sequence but keeping it independent.
Series: Integrating the jump directly into the sequence used for value estimation.

The Series approach (used in LS-Imagine) allows the gradients and value estimates from the "post-jump" future to flow back and inform the "pre-jump" actions. This is why the agent learns to orient itself toward targets even before it makes the jump.

Conclusion & Perspectives

LS-Imagine proves that we don't necessarily need more data or bigger models to solve open-world RL; we need smarter imagination. By encoding temporal leaps and spatial affordances into the world model, the agent gains a "telephoto lens" on its future.

Limitations: The current zoom-in heuristic for affordance is tailored to 3D navigation. Future work will need to generalize this to tasks like "driving" or "manipulation" where a simple zoom doesn't represent reaching a goal.

As an editor's note: This work bridges the gap between high-level planning (like LLM-based agents) and low-level visual control, providing a purely RL-based solution to long-horizon problems.

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Contents

LS-Imagine: Defeating Short-Sightedness in Open-World Reinforcement Learning

1. TL;DR

2. The "Short-Sighted" Agent Problem

3. Methodology: High-Speed Jumps in Latent Space

3.1. 1. The Long Short-Term World Model

3.2. 2. Affordance Maps via "Virtual Exploration"

3.3. 3. Jumpy State Transitions

4. Results: Efficiency in Action

5. Critical Insight: Series vs. Parallel Imagination

6. Conclusion & Perspectives