gym

name: gym description: 'Each gym domain resolves specific skill tensions:' metadata: skill_type: Environment/substrate for agent learning interface_ports: - Commands

gym Skill

Unified catalog of Gymnasium/OpenAI Gym environments for RL across all domains

Environment Taxonomy

                              ┌─────────────────────┐
                              │     GYMNASIUM       │
                              │   (OpenAI Gym API)  │
                              └──────────┬──────────┘
                                         │
         ┌───────────────┬───────────────┼───────────────┬───────────────┐
         │               │               │               │               │
    ┌────▼────┐    ┌─────▼─────┐   ┌─────▼─────┐   ┌─────▼─────┐   ┌─────▼─────┐
    │ PHYSICS │    │ ROBOTICS  │   │  ENERGY   │   │ CHEMISTRY │   │   GAMES   │
    └────┬────┘    └─────┬─────┘   └─────┬─────┘   └─────┬─────┘   └─────┬─────┘
         │               │               │               │               │
    MuJoCo          Isaac Gym       Microgrid       ChemistryGym      Atari
    PyBullet        RoboGym         PowerGrid       rlmolecule        NetHack
    dm_control      Softrobot       GEM (Motor)     SynthesisNet      Doom

Core Environments

Physics Simulation

import gymnasium as gym

# Classic control
env = gym.make("CartPole-v1")
env = gym.make("Pendulum-v1")
env = gym.make("Acrobot-v1")

# MuJoCo
env = gym.make("Humanoid-v4")
env = gym.make("Ant-v4")
env = gym.make("HalfCheetah-v4")

Robotics

# Isaac Gym (GPU parallel)
from omni.isaac.gym.vec_env import VecEnvBase
env = VecEnvBase(headless=True, num_envs=4096)

# Safe control
import safe_control_gym
env = gym.make("CartPole-v0", ctrl_freq=50)

Energy & Power Systems

# Microgrid (FMU-based)
env = gym.make('openmodelica_microgrid_gym:ModelicaEnv-v1',
               net='net/net.yaml',
               model_path='omg_grid/grid.network.fmu')

# Electric motor
import gym_electric_motor as gem
env = gem.make('Finite-SC-PermExcDC-v1')

# Power grid world
from gridworld import GridWorld
env = GridWorld(num_agents=3)

Chemistry & Molecular

# Chemistry Gym
from chemgym import ReactionEnv
env = ReactionEnv(vessels=2, max_steps=100)

# Molecule RL
from rlmolecule import MoleculeEnv
env = MoleculeEnv(target_property='logP')

# Distillation
from distillation_gym import DistillationEnv
env = DistillationEnv(num_components=3)

Games & Simulation

# Atari
env = gym.make("ALE/Breakout-v5")

# NetHack
import nle
env = gym.make("NetHackScore-v0")

# PufferLib (vectorized)
import pufferlib
env = pufferlib.make("atari_breakout")

Gymnasium API (Modern Standard)

import gymnasium as gym
from gymnasium import spaces

class CustomEnv(gym.Env):
    """Template for custom environment."""
    
    metadata = {"render_modes": ["human", "rgb_array"]}
    
    def __init__(self, render_mode=None):
        super().__init__()
        self.observation_space = spaces.Box(low=-1, high=1, shape=(4,))
        self.action_space = spaces.Discrete(2)
        self.render_mode = render_mode
    
    def reset(self, seed=None, options=None):
        super().reset(seed=seed)
        observation = self.observation_space.sample()
        info = {}
        return observation, info
    
    def step(self, action):
        observation = self.observation_space.sample()
        reward = 1.0
        terminated = False
        truncated = False
        info = {}
        return observation, reward, terminated, truncated, info
    
    def render(self):
        if self.render_mode == "rgb_array":
            return self._render_frame()
    
    def close(self):
        pass

Vectorized Environments

# Gymnasium native
envs = gym.vector.make("CartPole-v1", num_envs=8)

# Stable-Baselines3
from stable_baselines3.common.vec_env import SubprocVecEnv
envs = SubprocVecEnv([make_env(i) for i in range(8)])

# PufferLib (high-performance)
import pufferlib.vectorization
envs = pufferlib.vectorization.make(
    "CartPole-v1", num_envs=1024, backend="multiprocessing"
)

Wrappers

from gymnasium.wrappers import (
    TimeLimit,           # Max steps
    RecordVideo,         # Video recording
    NormalizeObservation,# Normalize obs
    NormalizeReward,     # Normalize rewards
    ClipAction,          # Clip actions
    FrameStack,          # Stack frames
    GrayscaleObservation,# Convert to grayscale
)

env = gym.make("CartPole-v1")
env = TimeLimit(env, max_episode_steps=500)
env = NormalizeObservation(env)

Skill Tension Resolution via Gyms

Each gym domain resolves specific skill tensions:

Gay.jl Integration

Color-code environments by domain:

GYM_COLORS = {
    'physics': '#63B6F0',    # Stream 3 (continuous)
    'robotics': '#89DF91',   # Stream 3 (embodied)
    'energy': '#E6F463',     # Stream 2 (temporal)
    'chemistry': '#5713C0',  # Stream 4 (synthesis)
    'games': '#CF6971',      # Stream 3 (discrete)
}

def color_for_env(env_id: str) -> str:
    if 'MuJoCo' in env_id or 'Pendulum' in env_id:
        return GYM_COLORS['physics']
    elif 'Isaac' in env_id or 'Robot' in env_id:
        return GYM_COLORS['robotics']
    elif 'Microgrid' in env_id or 'Motor' in env_id:
        return GYM_COLORS['energy']
    elif 'Chem' in env_id or 'Molecule' in env_id:
        return GYM_COLORS['chemistry']
    else:
        return GYM_COLORS['games']

Training Frameworks

# Stable-Baselines3
from stable_baselines3 import PPO
model = PPO("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=100000)

# RLlib
from ray.rllib.algorithms.ppo import PPOConfig
config = PPOConfig().environment("CartPole-v1")
algo = config.build()

# CleanRL (single file)
# python cleanrl/ppo.py --env-id CartPole-v1

Local Environments (from codebase)

Your codebase includes these custom gyms:

Neighbor Skills

• omg-tension-resolver: Microgrid gym for skill tension resolution
• alife: Artificial life environments
• gflownet: Sampling environments for molecule design
• forward-forward-learning: Local learning in environments
• safe-control-gym: Safety constraints in RL

Geometric Morphism Structure (Symplectic Bordism Core)

Secondary Symplectic Hub

This skill occupies a high-degree nexus in the skill-space network:

Flow Properties:

• In-degree: 6 (receives from 6 distinct morphism sources)
• Out-degree: 6 (sends to 6 distinct morphism targets)
• Symplectic Property: |in - out| = 0 ✓ (perfect flow balance)
• Status: SECONDARY SYMPLECTIC HUB (central RL environment nexus)

Morphism Neighbors (Discovered via Random Walk):

skill.gym ←→ skill.content-research-writer
          ←→ skill.file-organizer
          ←→ skill.omg-tension-resolver
          ←→ skill.entropy-sequencer
          ←→ skill.forward-forward-learning
          ←→ skill.gflownet

Interpretation

The gym environment ecosystem represents the practical instantiation of reinforcement learning and embodied reasoning:

• Type: Environment/substrate for agent learning
• Role: Central locus where theory meets practice
• Topology: Bridges discrete (games) and continuous (physics) domains
• Symplectic Property: Preserves phase-space volume across all environment types

Its perfect 6→6 balance means it acts as an orchestration hub—a distribution center where conceptual flows enter (ideas from higher-level skills) and exit (instantiated environments).

Coherence Proof

Theorem (Secondary Hub Property):
  skill.gym is symplectic ⟺ in-deg(gym) = out-deg(gym) = 6

Proof:
  by direct inspection of morphism graph
  ∑ in-flow(gym) = ∑ out-flow(gym) = 6 ✓

Corollary (Orchestration):
  For any composition φ: X → Y through gym,
  the morphism is bijective:
    |φ⁻¹({gym})| = |φ({gym})| = 6

Cross-Skill Integration

This skill links seamlessly to:

• content-research-writer: Synthesizes domain knowledge for environment design
• file-organizer: Structures environment catalogs and benchmarks
• omg-tension-resolver: Resolves skill tensions through gym domains
• entropy-sequencer: Sequences environment complexity levels
• forward-forward-learning: Local learning without backprop (gym-compatible)
• gflownet: Flow-matching for molecule design via chemical gyms

Resources

• Gymnasium Docs
• Farama Foundation - Maintainers
• Awesome RL Envs
• PettingZoo - Multi-agent
• Symplectic Bordism Core — Full geometric morphism analysis

End-of-Skill Interface

Commands

# Install gymnasium
pip install gymnasium[all]

# Install domain-specific
pip install gymnasium[mujoco]
pip install gymnasium[atari]
pip install gym-electric-motor
pip install openmodelica_microgrid_gym

# List available envs
python -c "import gymnasium; print(gymnasium.envs.registry.keys())"

# Run with rendering
python -c "
import gymnasium as gym
env = gym.make('CartPole-v1', render_mode='human')
env.reset()
for _ in range(1000):
    env.step(env.action_space.sample())
env.close()
"

Autopoietic Marginalia

The interaction IS the skill improving itself.

Every use of this skill is an opportunity for worlding:

• MEMORY (-1): Record what was learned
• REMEMBERING (0): Connect patterns to other skills
• WORLDING (+1): Evolve the skill based on use

Add Interaction Exemplars here as the skill is used.