Reinforcement Learning Evaluation Environments

Enable large-scale reinforcement learning evaluation through concurrent execution of policy episodes in hundreds of independent DVM sandboxes.

Objective: Large-Scale Reinforcement Learning Evaluation

Enable Decentra to assess reinforcement learning policies at scale by executing hundreds of evaluation episodes in parallel across isolated DVM sandboxes. While policy optimization and training occur on external GPU-based infrastructure, sandboxed execution provides safe, repeatable environments for policy evaluation and reward collection.

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Distributed Policy Evaluation with DVM Sandboxes

DVM sandboxes allow reinforcement learning policies to be evaluated concurrently by provisioning large numbers of isolated execution environments. Each sandbox runs a single evaluation episode independently, capturing rewards and performance metrics without shared state. This design is well-suited for RL workflows where training requires GPUs, but evaluation can be efficiently distributed across CPU-based infrastructure to rapidly gather statistically meaningful results.

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Why Parallel Evaluation Matters

Reliable reinforcement learning evaluation typically requires thousands of episodes, which can be slow and costly when executed sequentially. By leveraging sandboxed parallelism, Decentra can:

• Scale evaluation throughput Launch hundreds of DVM sandboxes simultaneously to evaluate policies in parallel, dramatically shortening evaluation cycles.

• Collect reward signals efficiently Each sandbox produces independent reward data that can be aggregated and fed back into external training pipelines.

• Guarantee environment isolation Ensure each evaluation episode runs independently, preventing cross-interference and preserving metric integrity.

• Accelerate iteration loops Rapidly assess updated policies from external training systems, enabling faster experimentation and refinement.

• Optimize infrastructure costs Reserve GPU resources exclusively for training while using CPU-based sandboxes for large-scale evaluation.

This architecture allows Decentra to support reinforcement learning workflows that require fast, reliable feedback without overloading expensive training infrastructure.

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Practical Applications

Recommendation Systems

Personalization models can be evaluated by running hundreds of recommendation scenarios in parallel sandboxes, collecting engagement or conversion rewards for downstream optimization.

Autonomous Decision Systems

Business logic and decision policies can be tested through concurrent simulations of operational scenarios, producing performance metrics for external training refinement.

Game Intelligence

Strategy policies can be evaluated by executing thousands of parallel game episodes in DVM sandboxes, gathering win rates, scores, and behavioral statistics to guide learning.

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Scenario: Concurrent Policy Assessment

Decentra is training a reinforcement learning policy on external GPU infrastructure. After each training update, it initiates a large-scale evaluation phase by provisioning 500 DVM sandboxes. Each sandbox runs an independent evaluation episode using the updated policy and records reward outcomes. All results are aggregated and transmitted back to the external training system, providing immediate, statistically robust feedback for the next optimization step.

Implementation: Concurrent Evaluation Pipeline

1

Receive Policy Update

Agent receives updated policy weights from external GPU-based training system.

2

Spawn Evaluation Sandboxes

Agent creates hundreds of sandboxes, each configured to run an evaluation episode.

3

Distribute Policy

Agent distributes the policy weights to all evaluation sandboxes.

4

Run Concurrent Evaluations

All sandboxes execute evaluation episodes simultaneously, collecting rewards independently.

5

Collect Rewards

Agent gathers reward data from all completed evaluation episodes.

6

Aggregate Metrics

Agent computes average rewards, variance, and other performance metrics.

7

Feed Back to Training

Agent sends aggregated reward metrics to external training system for policy optimization.

Example (TypeScript)

evaluate-policy.ts

import SandboxSDK from '@dvmcodes/sandbox-sdk';

const client = new SandboxSDK({
  apiKey: process.env['SANDBOX_SDK_API_KEY'],
});

async function evaluateRLPolicy(
  policyWeights: string,
  numEvaluations: number = 500
) {
  // Spawn hundreds of sandboxes for concurrent evaluation
  const sandboxes = await Promise.all(
    Array.from({ length: numEvaluations }, (_, i) =>
      client.sandboxes.create({
        name: `RL Evaluation ${i + 1}`,
        resources: {
          cpus: 2,
          memory: 512,
        },
      })
    )
  );

  // Run concurrent evaluation episodes
  const evaluationPromises = sandboxes.map((sandbox, idx) =>
    client.sandboxes.execute(sandbox.id, {
      command: `python evaluate_policy.py --weights "${policyWeights}" --episode ${idx}`,
      env: {
        POLICY_WEIGHTS: policyWeights,
      },
      timeout: 300,
    })
  );

  // Collect rewards from all evaluations
  const results = await Promise.all(evaluationPromises);
  const rewards = results
    .filter(r => r.status === 'completed' && r.exit_code === 0)
    .map(r => {
      // Parse reward from stdout
      const output = JSON.parse(r.stdout);
      return output.reward;
    });

  // Aggregate metrics for external training system
  const avgReward = rewards.reduce((a, b) => a + b, 0) / rewards.length;
  const variance =
    rewards.reduce((sum, r) => sum + Math.pow(r - avgReward, 2), 0) /
    rewards.length;

  return {
    averageReward: avgReward,
    variance: variance,
    numEpisodes: rewards.length,
    rewards: rewards,
  };
}

Next Steps

• Integrate with external GPU training pipelines Connect Decentra with GPU-based learning frameworks such as PyTorch and TensorFlow to exchange policies and reward feedback seamlessly.

• Introduce adaptive evaluation scaling Dynamically adjust the number of DVM sandboxes based on observed policy variance and confidence thresholds to balance speed and statistical reliability.

• Implement reward aggregation and analytics Aggregate rewards from parallel evaluations and compute statistical metrics (means, variance, confidence intervals) to support informed training updates.

• Add evaluation result caching Cache recent evaluation outputs to avoid redundant execution and enable faster iteration when policies or environments have not materially changed.