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LoomOS Reinforcement Learning System

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Architecture Overview

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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

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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

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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

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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

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Design Principles

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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

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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

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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

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Loom RL Gym

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Environment Registry & Versioning

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
)

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Custom Environment SDK

from rl.envs import BaseEnvironment

class MyEnv(BaseEnvironment):
	def __init__(self, config):
		super().__init__(config)
		# ...
	def step(self, action):
		# ...
	def reset(self):
		# ...

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)

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PPO Trainer

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Distributed PPO

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)

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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

from rl import PPOTrainer, PPOConfig

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

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WEAVE algorithm

• Multi-objective reward shaping (safety, task completion, creativity)
• Ensemble critics for robust value estimation
• Node specialization across cluster for mixed objectives

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)

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Distributed training patterns

• Data-parallel rollouts with centralized updates
• Parameter-server-less gradient aggregation (all-reduce/NCCL)
• Curriculum learning via environment progression

• Monitor KL divergence to avoid policy collapse
• Use early stopping when validation reward stagnates
• Leverage LoomDB event traces to debug reward shaping issues