Skip to main content

​
LoomOS Reinforcement Learning System

The LoomOS Reinforcement Learning System provides a comprehensive, production-ready infrastructure for training, evaluating, and deploying reinforcement learning agents at scale. Built on distributed computing principles, it supports everything from simple single-agent environments to complex multi-agent systems with hierarchical objectives.

​
Architecture Overview

The RL System is composed of four primary layers working in concert:

​
1. Environment Layer - LoomRL Gym

  • Multi-Environment Support: Standardized interface for diverse RL environments
  • Environment Registry: Centralized catalog of available environments and configurations
  • Custom Environment SDK: Tools for creating domain-specific environments
  • Environment Versioning: Track environment changes and maintain reproducibility

​
2. Algorithm Layer - Advanced RL Algorithms

  • PPO (Proximal Policy Optimization): State-of-the-art policy gradient method
  • WEAVE Algorithm: Multi-objective RL with ensemble critics and weighted exploration
  • SAC (Soft Actor-Critic): Off-policy algorithm for continuous control
  • Custom Algorithm Framework: Plugin architecture for custom RL algorithms

​
3. Training Layer - Distributed Orchestration

  • Distributed Rollout Collection: Parallel environment interaction across clusters
  • Gradient Aggregation: Efficient parameter synchronization using NCCL
  • Experience Replay: Scalable experience buffer management
  • Curriculum Learning: Automated environment progression and difficulty adjustment

​
4. Evaluation & Deployment Layer

  • Policy Evaluation: Comprehensive agent performance assessment
  • A/B Testing Framework: Compare different policy versions in production
  • Online Learning: Continuous policy improvement from production data
  • Safety Monitoring: Real-time policy behavior analysis and intervention

​
Design Principles

​
Reproducibility

  • Deterministic Training: Consistent random seeds and deterministic operations
  • Comprehensive Checkpointing: Save and restore complete training state
  • Artifact Provenance: Complete lineage tracking in LoomDB
  • Environment Versioning: Immutable environment snapshots

​
Scalability

  • Horizontal Scaling: Distribute training across hundreds of nodes
  • Efficient Communication: Optimized gradient aggregation and parameter broadcast
  • Dynamic Load Balancing: Automatic resource allocation based on demand
  • Memory Optimization: Efficient experience replay and model storage

​
Safety & Verification

  • Integrated Safety Checks: Built-in policy behavior verification
  • Prism Integration: Comprehensive safety and compliance validation
  • Rollback Mechanisms: Automatic reversion to safe policies
  • Real-time Monitoring: Continuous policy performance tracking

​
Loom RL Gym

The RL Gym exposes a consistent environment API and integrates with the rest of the platform for logging and metrics.

​
Environment Registry & Versioning

All environments are registered in LoomDB with full versioning and metadata. This enables reproducible experiments and audit trails. 
from rl import LoomRLGym, register_environment

register_environment(
	name="CustomTradingEnv",
	entry_point="trading_envs:TradingEnvironment",
	version="1.2.0",
	max_episode_steps=1000,
	reward_threshold=500.0
)

​
Custom Environment SDK

Create domain-specific environments using the SDK: 
from rl.envs import BaseEnvironment

class MyEnv(BaseEnvironment):
	def __init__(self, config):
		super().__init__(config)
		# ...
	def step(self, action):
		# ...
	def reset(self):
		# ...
Basic usage 
from rl import LoomRLGym, EnvironmentType

gym = LoomRLGym()
math_env = await gym.create_environment(EnvironmentType.MATH, {"difficulty": "intermediate", "max_steps": 50})
state = await math_env.reset()
action = 0
next_state, reward, done, info = await math_env.step(action)

​
PPO Trainer

PPO implements clipped surrogate objectives with GAE and supports distributed updates across nodes. Key config knobs:

​
Distributed PPO

LoomOS supports distributed PPO with NCCL-based gradient aggregation and parameter broadcast. Example: 
from rl import PPOTrainer, PPOConfig

config = PPOConfig(
	learning_rate=3e-4,
	batch_size=4096,
	n_epochs=20,
	distributed=True,
	sync_frequency=100,
	log_interval=10
)
trainer = PPOTrainer(config)
await trainer.train(total_timesteps=10_000_000)

​
Advanced PPO Features

  • Adaptive learning rate schedules
  • Entropy regularization for exploration
  • Early stopping and checkpointing
  • Integration with LoomDB for experiment tracking
  • learning_rate, batch_size, n_epochs, clip_epsilon, value_loss_coef, entropy_coef
Example configuration 
from rl import PPOTrainer, PPOConfig

config = PPOConfig(learning_rate=3e-4, batch_size=2048, n_epochs=10, clip_epsilon=0.2)
trainer = PPOTrainer(config)

​
WEAVE algorithm

WEAVE (Weighted Exploration, Adaptive Value Estimation) provides hierarchical and multi-objective training. Use cases:
  • Multi-objective reward shaping (safety, task completion, creativity)
  • Ensemble critics for robust value estimation
  • Node specialization across cluster for mixed objectives
Mathematical summary 
π_WEAVE(a|s) = Σ w_i(s) * π_i(a|s)
V_WEAVE(s) = (1/Z) * Σ α_j(s) * V_j(s)
R(s,a) = Σ β_h * R_h(s,a)

​
Distributed training patterns

  • Data-parallel rollouts with centralized updates
  • Parameter-server-less gradient aggregation (all-reduce/NCCL)
  • Curriculum learning via environment progression
Troubleshooting & tips
  • Monitor KL divergence to avoid policy collapse
  • Use early stopping when validation reward stagnates
  • Leverage LoomDB event traces to debug reward shaping issues