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RustForge Python

Native environments and CPU DQN, with an optional Gymnasium bridge. Import rustforge; the package distribution is rustforge-rl (CPython 3.9+).

pip install rustforge-rl
pip install "rustforge-rl[gym]"  # optional Gymnasium support

To use the new convenience functions from this checkout, activate a virtualenv, install maturin and run maturin develop in this directory.

import rustforge as rf

print(rf.available_envs())
env = rf.make_env("CartPole-v1")
agent = rf.train("cartpole", episodes=10, output="dqn.csv")
action = agent.predict(env.reset(42))

# Optional Gymnasium: float32 NumPy observations and standard tuples.
env = rf.make_env("pendulum", api="gymnasium")
obs, info = env.reset(seed=42)

make_env defaults to native reset/step tuples. Environment names accept case, hyphens/underscores and supported Gym ids. Native classes, DQN.train and Gymnasium make remain available. Python DQN supports CartPole, GridWorld and MountainCar; use the CLI for other algorithms and GPU training. Episode-limit defaults in train are 500, 100 and 200 respectively. Training randomness is not fully seed-controlled.

output accepts strings or pathlib paths. Invalid training settings raise ValueError before opening output; training/log I/O failures raise RuntimeError. Native negative discrete actions raise OverflowError; out-of-range actions and invalid action dimensions raise ValueError.

Workflow details · Project README

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