Zero-Shot Reinforcement Learning

• Zero-shot reinforcement learning is a paradigm where agents generalize to new tasks without additional training.
• Key methodologies, such as compositional representations and successor features, enable immediate policy adaptation to novel rewards and environments.
• Empirical benchmarks demonstrate that approaches like forward-backward and conservative methods yield significant improvements in simulation and real-world control settings.

Zero-shot reinforcement learning (RL) is a paradigm in which agents are trained with the explicit goal of producing policies that immediately generalize to new tasks or environments without any additional training, adaptation, or planning at test time. This setting diverges from conventional RL, which requires learning anew for each different reward or environment. Zero-shot RL thus demands that pre-trained agents encode representations rich enough to facilitate instant adaptation upon presentation of a novel reward function or context, closely paralleling developments in vision and language foundation models (Ventura et al., 23 Oct 2025). Key methodologies include compositional value decompositions, unsupervised environment design, robust representation learning, hypernetworks, and advances in offline and partially observable RL. The field now encompasses both purely theoretical formulations and large-scale empirical studies across simulation, robotics, scientific computing, and real-world control.

1. Formal Frameworks and Taxonomies

Zero-shot RL is formalized over a family of Markov Decision Processes (MDPs) parametrized by reward and/or transition dynamics:

$$\mathcal{M}^R = \{ (S, A, p, p_0, r, \gamma) \mid r \in \mathcal{R} \}$$

Agents are characterized by mappings

$\pi: \mathcal{R} \to \Pi, \qquad r \mapsto \pi_r$

that assign a policy for any newly encountered reward function (Ventura et al., 23 Oct 2025). The field divides algorithms into two major families:

• Direct Representations: End-to-end learning of universal value or policy networks, with task/reward embedding as input. These methods typically rely on a supervised loss over sampled rewards at train time.
• Compositional Representations: Decompose the value function by linearity or measure-theoretic structure—e.g., successor features, forward-backward measures, operator-based networks—allowing for truly reward-free pretraining and explicit policy inference at test time.

Orthogonal taxonomies classify approaches by training regimen (reward-free vs. pseudo reward-free), representation expressivity, and test-time inference cost. Extended theoretical bounds have clarified the trade-offs in successor-feature methods regarding feature linearization, codebook coverage, and value approximation error (Ventura et al., 23 Oct 2025).

2. Key Representation Learning Techniques

Successor features (SFs) and Forward–Backward (FB) representations have become foundational for zero-shot RL:

• SFs construct a feature map $\phi$ and learn its expected discounted visitation under policy $\pi$, such that for any linear reward $r(s) = \phi(s)^\top w$, $Q^\pi_r(s,a) = \psi^\pi(s,a)^\top w$. At test time, for a new reward, one solves for $w$ and executes the corresponding greedy policy (Touati et al., 2022).
• FB representations jointly learn two networks $F$ and $B$ such that the successor measure $$M^{\pi_z}(s,a,ds') \approx F(s,a,z)^\top B(s')\,\rho(ds')$$; the zero-shot policy for any $r$ is then derived via $z = \mathbb{E}_\rho[r(s)B(s)]$, and $\pi_z(s) = \arg\max_a F(s,a,z)^\top z$ (Touati et al., 2022).

Recent algorithms enhance expressivity by integrating behavioral regularization (curbing OOD action bias), diffusion-modelling for multimodal action generation, and sophisticated attention mechanisms within $F$ and $B$ (Zheng et al., 17 Oct 2025). The function-encoder framework projects any new reward or transition function onto learned nonlinear bases, enabling plug-in context for general RL networks (Ingebrand et al., 2024). Operator Deep Q-Learning encodes the Bellman operator as a neural mapping from rewards to value functions, guaranteeing instant value estimation for arbitrary reward functions (Tang et al., 2022).

3. Strategies for Robust Generalization

Zero-shot RL performance hinges on the agent successfully "controlling" for unseen tasks with minimal practice. Recent work has exposed two major challenges: performance degradation when pre-training data is of low quality (small size, narrow coverage) and instability under partial observability (Jeen et al., 2023, Jeen et al., 18 Jun 2025, Jeen, 22 Aug 2025). Value-conservative and measure-conservative variants of FB representations (VC-FB and MC-FB) introduce additional penalties in the training loss to suppress out-of-distribution overestimation, yielding up to $1.5 \times$ gains on low-quality datasets and no performance loss for large datasets (Jeen et al., 2023). Memory-augmented architectures equip FB and SF methods with GRU-based temporal encoding, restoring near-oracle performance under state noise, flickering observations, and dynamic shifts (Jeen et al., 18 Jun 2025, Jeen, 22 Aug 2025).

Environment design regularization approaches, such as prioritized level replay and data-regularized generative modelling (DRED), employ mutual-information bottlenecks and generative models to sample levels that balance coverage against overfitting, substantially improving zero-shot generalization on tasks with procedurally generated environments (Garcin et al., 2024).

4. Zero-Shot RL in Contextual and Physical Domains

Zero-shot RL has been demonstrated to generalize across a spectrum of contextual Markov decision processes (CMDPs) and control domains. Joint context-policy learning, where the context encoder is updated by RL objectives rather than auxiliary predictive loss, achieves superior interpolation and extrapolation to unseen environmental parameters (e.g., gravity, pendulum length, mass) (Ndir et al., 2024). The context-enhanced Bellman Equation (CEBE), with context-sample enhancement (CSE), analytically achieves first-order accurate generalization from a single training context, via a principled Taylor expansion (Chapman et al., 10 Jul 2025). Hypernetwork-based approaches model the RL mapping from environment context (reward and dynamics parameters) to policy/value directly as a supervised learning problem, supporting zero-shot transfer to new reward/dynamics combinations (Rezaei-Shoshtari et al., 2022).

In robotics and control, zero-shot RL strategies have yielded 95% out-of-the-box success in high-dimensional guidewire navigation given only minimal basis training, and achieved competitive autonomous exploration under uncertainty in SLAM settings, by encoding domain knowledge via graph neural networks and local, invariant feature spaces (Scarponi et al., 2024, Chen et al., 2021).

5. Empirical Benchmarks and Performance Analysis

Extensive evaluations across ExORL, D4RL, DeepMind Control Suite, Minigrid, Procgen, and scientific computing validate the effectiveness and limitations of zero-shot RL methods. FB and VC-FB/MC-FB outperform both SF variants and single-task conservative offline RL in heterogeneous and resource-constrained datasets (Jeen et al., 2023, Jeen, 22 Aug 2025). BREEZE (behavior-regularized FB with task-conditioned diffusion policy) attains top or near-top interquartile mean returns in 11 of 12 ExORL domains and remains robust in the small-sample regime (Zheng et al., 17 Oct 2025). TD-JEPA, a latent-predictive temporal-difference method, achieves state-of-the-art zero-shot generalization from pixel inputs, matching or exceeding Laplacian-based, Hilbert, and FB pipelines (Bagatella et al., 1 Oct 2025). Function encoder approaches match oracle baseline performance and attain marked improvements in multi-agent and multi-task reinforcement learning (Ingebrand et al., 2024).

The field recognizes the importance of detailed theoretical analyses: the zero-shot RL loss can be directly optimized for a variety of reward priors (white noise, Dirichlet-smooth, sparse/goal-oriented), where end-to-end feature learning (as in VISR) recovers the zero-shot objective and reveals pitfalls such as sharp/polar-optimal policies under Gaussian priors (Ollivier, 15 Feb 2025). Empirical limitations include dependence on offline data diversity, instability under domain extrapolation, and computational cost in large-scale iterative planning (e.g., MPPI in building control; PEARL matches oracle emission reduction with only three hours of active exploration, but with higher planning latency (Jeen et al., 2022, Jeen et al., 2022)).

6. Current Challenges and Research Directions

Several open challenges remain. Zero-shot RL methods must further ameliorate:

• Dataset quality constraints: Effective regularization and coverage augmentation enable robust learning from small, homogeneous datasets, but further advances are needed for ultra-low data settings and environments with extreme local invariance (Jeen, 22 Aug 2025, Garcin et al., 2024).
• Partial observability/generalization: Memory-augmented models demonstrate promise, but optimal memory architectures and belief-state representations for large-scale POMDPs remain under investigation (Jeen et al., 18 Jun 2025, Jeen, 22 Aug 2025).
• Environment/model misalignment: Realistic deployment settings require handling simulators with systematic or pathological bias, a primary focus of recent empirical studies.
• Algorithmic scalability: Policy extraction via diffusion models, efficient operator networks, and hypernetwork architectures present promising avenues for scaling toward RL foundation models (Zheng et al., 17 Oct 2025, Rezaei-Shoshtari et al., 2022, Tang et al., 2022).
• Benchmark standardization: New tasks specifically designed to stress compositional and direct representation paradigms may clarify the frontier strengths and failure modes of existing methods (Ventura et al., 23 Oct 2025).

A plausible implication is that future zero-shot RL agents will integrate mutual-information regularization, robust offline conservatism, expressive context and task encoding, and adaptive environment generation to achieve reliable, scalable, and efficient instant generalization—closing the gap with supervised RL and supporting deployment in domains with stringent data or observability constraints.