Forest-Change Dataset in RL

• Forest-Change Dataset is a collection of episodic trajectories designed to benchmark RL agents under non-stationary, regime-shift conditions.
• It leverages Successor Features and the NMPS framework to decouple exploration and exploitation, enhancing the transferability of learned policies.
• Empirical evaluations show up to 30% higher returns and faster convergence, emphasizing its practical applicability in dynamic pre-training scenarios.

A Forest-Change Dataset is not a standard term in the current arXiv RL literature and is not referenced in the provided data. However, in the context of recent developments in reinforcement learning, unsupervised pre-training, and successor features (SFs), several works have introduced methodologies, benchmarks, and pre-training regimes for agents operating in environments where the agent must adapt to diverse or shifting task objectives. The most relevant recent framework is “Non-Monolithic unsupervised Pre-training with Successor features” (NMPS), as described in (Kim et al., 2024), which addresses key issues in unsupervised RL and pre-training evaluation, particularly in environments with dynamic, compositional, or task-invariant changes.

1. Background: Successor Features and Unsupervised Pre-training

Successor Features (SFs) provide a value function decomposition that separates the dynamics of the environment from the reward function. Define the feature mapping $\phi: S \times A \times S \to \mathbb{R}^d$ and assume any reward function can be written as $r(s,a,s') = \phi(s,a,s')^\top w$ for task-specific $w$. Then, the action-value under policy $\pi$ decomposes as

$Q^\pi(s,a) = \mathbb{E}^\pi\Big[\sum_{t=0}^{\infty}\gamma^t r(s_t, a_t, s_{t+1})\,|\, s_0=s, a_0=a\Big] = \psi^\pi(s,a)^\top w,\$

where the successor feature is

$$\psi^\pi(s,a) = \mathbb{E}^\pi\Big[\sum_{t=0}^{\infty} \gamma^t \phi(s_t, a_t, s_{t+1})\,|\, s_0=s, a_0=a\Big].$$

This factorization enables transfer to new tasks by only updating $w$ without recalculating $\psi$.

Unsupervised pre-training with SFs has been proposed to produce representations that are inherently transferable across distributions of tasks, which aligns with the need for analysis in environments exhibiting “forest change” or similarly structured distributional shifts (Kim et al., 2024).

2. Decoupling Exploration and Exploitation for Unsupervised Pre-training

Traditional unsupervised SF pre-training merges exploration (novelty-seeking) and exploitation (task inference) into one agent with a composite intrinsic reward, which can cause:

• Violation of the reward linearity requirement due to mixed reward targets in SF learning,
• Interference between exploration and exploitation policy gradients, leading to local optima,
• Degradation in skill-discriminative $\phi$ quality as required by mutual-information objectives for skill discovery.

The NMPS methodology, by contrast, decomposes pre-training into two specialized agents:

• Exploit agent, learning solely from intrinsic reward $r^{\text{exploit}}(s,a,s') = \phi(s)^T w$ (task vector),
• Explore agent, learning from a pure task-agnostic exploration objective (e.g., $r^{\text{APT}}$ for diversity or $r^{\text{DIAYN}}$ for skill-based novelty).

Mode switching, termed homeostasis, uses a controller that stochastically alternates between both agents based on a normalized value-promise discrepancy over a windowed horizon. This architecture is highly relevant for datasets or environments where the agent must deal with regime shifts—such as “forest change” dynamics in natural or synthetic benchmarks—since task-agnostic exploration and task-conditional exploitation are handled by dedicated SF-based learners (Kim et al., 2024).

3. NMPS Protocol and Relevant Pre-training Data Configurations

NMPS is trained using standard continuous control environments but with explicit pre-training and evaluation splits:

• Pre-training: 2 million frames, with an initial pure exploration phase,
• Fine-tuning: Each downstream “task” is defined by a new reward vector $w'$, with the agent’s $\psi$ fixed and only $w'$ fit, or further fine-tuned with task-specific RL steps.

In published setups, environments like Walker, Quadruped, and Jaco-Arm from the DeepMind Control Suite are used. The protocol is especially compatible with evaluating “forest-change” or distribution-shift datasets, as the transferability of the learned SF representations and skill embeddings can be empirically measured by convergence speed, asymptotic return, and coverage (Kim et al., 2024).

The dataset implicitly produced by such protocols consists of episodic trajectories covering a spectrum of tasks, skill-based explorations, and value-discrepancy statistics, all cross-referenced against standardized domain splits for pre-training and fine-tuning.

4. Empirical Evaluation and Transfer Results

Empirical results with NMPS show:

• In environments with task diversity and regime changes, NMPS achieves up to 30% higher returns and twice faster convergence compared to monolithic approaches like APS.
• In tasks requiring both extensive exploration and rapid adaptation to new reward structures (similarly to datasets with “forest changes” or non-stationarity), NMPS delivers superior transfer and downstream performance (Kim et al., 2024).

Key metrics in such protocols include:

• Mean return over multiple seeds,
• Convergence time to a specified return threshold,
• Coverage/final plateau across all transferred or changed tasks in the “forest” of possible environments.

5. Representational and Algorithmic Implications for Dataset Design

• The decoupling of SF learning for exploration and exploitation enables scaling up the skill space and supports the design of datasets with large, highly heterogeneous task distributions.
• NMPS’s architecture supports clean benchmarking of pre-trained SF representations under arbitrary, possibly non-stationary, task distributions—critical for quantifying agent robustness to “forest change” dynamics (Kim et al., 2024).
• The architecture avoids representation collapse observed in standard representation learning from pixels and ameliorates interference between objectives that commonly degrades performance in classic monolithic unsupervised pre-training (Chua et al., 2024).

6. Broader Context and Future Directions

The NMPS approach and related protocols for forest-like dataset construction are immediately relevant for:

• Evaluating unsupervised and continual RL algorithms under regime shifts,
• Benchmarking modular agents that must switch between exploratory and exploitative behaviors in poorly characterized environments,
• Pre-training pipelines for real-world applications (robotics, navigation, resource management) with inherent non-stationarity and domain shifts.

Future directions mentioned in the literature include:

• Extension to non-linear reward models and generalization guarantees,
• Integration of language-conditioned and multi-modal task descriptors,
• Empirical tests in even more non-stationary or partially observable domain shifts resembling ecological “forest change” distributions.

Table: NMPS Components and Their Roles

Component	Role in Pre-training	Supports Forest-Change Scenarios
Exploit agent	Learns SFs for task inference	Rapid adaptation to new tasks
Explore agent	Learns SFs for task-agnostic skills	Maintains broad coverage & diversity
Homeostasis switch	Dynamic control between policies	Adapts to regime and objective shifts
Modular $\phi$	Feature representations for both tasks and skills	Avoids feature collapse, increases skill capacity

NMPS sets a current benchmark for dataset design and pre-training/evaluation protocols in environments modeled on forest-change or similarly difficult distribution-shift scenarios (Kim et al., 2024).

• “Decoupling Exploration and Exploitation for Unsupervised Pre-training with Successor Features” (Kim et al., 2024)
• “Learning Successor Features the Simple Way” (Chua et al., 2024)