SVL:
Goal-Conditioned Reinforcement Learning as Survival Learning

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Survival Value Learning (SVL) is a probabilistic alternative to TD-based goal-conditioned reinforcement learning. SVL reframes GCRL as a survival learning problem by modeling the time-to-goal from each state as a probability distribution, and derives a closed-form identity that expresses the goal-conditioned value function as a discounted sum of survival probabilities. Values are estimated via a hazard model trained by maximum likelihood on both event and right-censored trajectories, with three practical estimators (finite-horizon truncation and two binned infinite-horizon approximations) to capture long-horizon objectives.

This repository implements HSVL, the hierarchical variant evaluated in the paper: SVL value estimation paired with an AWR-style hierarchical actor (high-level subgoal proposer + low-level controller). HSVL matches or surpasses strong hierarchical TD and Monte Carlo baselines on the long-horizon locomotion and navigation tasks from OGBench.

Installation

We manage the project with uv. From the repo root:

uv sync

This creates .venv/ and installs all dependencies pinned in uv.lock (including the right JAX + CUDA wheels on Linux).

Usage

Configuration is managed with Hydra. The entry point is train.py at the repo root, and all knobs can be overridden from the command line.

# Default run (antmaze-large-navigate)
uv run train.py

# Override the environment
uv run train.py env_name=humanoidmaze-large-navigate-v0 \
    agent.subgoal_steps=100 \
    agent.num_log_bins=500

# humanoidmaze-giant uses a higher discount
uv run train.py env_name=humanoidmaze-giant-navigate-v0 \
    agent.discount=0.999 \
    agent.subgoal_steps=100 \
    agent.num_log_bins=500

Multiple seeds / sweeps

Use Hydra multi-run (-m) to dispatch several runs:

# Two seeds
uv run train.py -m env_name=humanoidmaze-giant-navigate-v0 \
    agent.discount=0.999 \
    agent.subgoal_steps=100 \
    agent.num_log_bins=500 \
    seed=0,1

With the submitit launcher (configured via hydra/launcher), each combination is submitted as a separate SLURM job. Locally, runs are executed sequentially.

Citing SVL

@misc{tiofack2026svlgoalconditionedreinforcementlearning,
    title={SVL: Goal-Conditioned Reinforcement Learning as Survival Learning},
    author={Franki Nguimatsia Tiofack and Fabian Schramm and Théotime Le Hellard and Justin Carpentier},
    year={2026},
    eprint={2604.17551},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url={https://arxiv.org/abs/2604.17551},
}