Use the Gymnasium adapter¶

Wrap OrbitalGymEnv as a gymnasium.Env for single-agent RL training.

Recipe¶

import numpy as np
from orbitalgym import OrbitalGymEnv, make_lady_bandit_guard
from orbitalgym.adapters.gymnasium import GymnasiumAdapter

cfg = make_lady_bandit_guard()
env = GymnasiumAdapter(OrbitalGymEnv(cfg), seed=0)

obs, info = env.reset(seed=0)
done = False
total = 0.0
while not done:
    action = np.zeros(env.action_space.shape, dtype=np.float32)
    obs, reward, terminated, truncated, info = env.step(action)
    total += reward
    done = terminated or truncated

Spaces¶

Both action_space and observation_space are unbounded Box(float32). The action shape is flat: (n_controlled * action_dim,). The adapter accepts either flat or (n_controlled, action_dim)-shaped input and reshapes internally.

For the bundled FullObservation, the observation shape is the layout's flat dim.

Termination semantics¶

terminated reflects episode_done. Both max_steps exhaustion and game-specific conditions count as termination, so truncated is always False. The env's TerminationFn already handles the step limit.

Switching the controlled side¶

See Switch the controlled side.

Customising the opponent¶

See Customize the opponent.

See also¶

• API reference → Adapters.