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Reinforcement Learning Evaluation Environments

AI agents can evaluate reinforcement learning policies at massive scale by running concurrent evaluation episodes across hundreds of isolated sandboxes. While policy training happens externally on GPU-enabled infrastructure, sandboxes provide the isolated environments needed to evaluate policies and collect rewards, enabling rapid iteration and validation of trained models.

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Power of Sandboxes

Sandboxes enable massive parallel evaluation of RL policies by spawning hundreds of concurrent environments. Each sandbox runs an isolated evaluation episode, collecting rewards and metrics independently. This concurrent evaluation capability is critical for RL workflows where training happens externally (requiring GPU resources), but evaluation can be distributed across many CPU-based sandboxes to gather statistically significant reward data quickly.

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Why It Makes Agents Better

Evaluating RL policies typically requires running thousands of episodes to get reliable performance metrics, which can be slow and resource-intensive. With sandboxes, agents can:

• Scale evaluation: Spawn hundreds of concurrent sandboxes to evaluate policies in parallel, dramatically reducing evaluation time
• Collect rewards efficiently: Each sandbox runs an independent evaluation episode, providing reward feedback for external training systems
• Isolate environments: Each evaluation runs in complete isolation, preventing interference and ensuring accurate metrics
• Rapid iteration: Quickly evaluate policy updates from external training, enabling faster training loops
• Cost-effective: Use CPU-based sandboxes for evaluation while keeping GPU resources focused on training

This enables agents to efficiently evaluate RL policies at scale, providing the reward feedback needed for external GPU-based training systems to optimize policies effectively.

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Use Cases

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Recommendation System Agents

Agents training personalization models can evaluate policies by running hundreds of concurrent recommendation scenarios in sandboxes, collecting user engagement rewards to feed back into external GPU-based training.

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Autonomous Decision-Making Agents

Business process agents can evaluate decision-making policies by running concurrent simulations of business scenarios, collecting performance metrics for external training optimization.

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Game AI Agents

Agents developing game strategies can evaluate policies by running thousands of concurrent game episodes in sandboxes, collecting win rates and scores to guide external training.

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Scenario: Concurrent Policy Evaluation

An agent is training an RL policy externally on GPU infrastructure. After each training update, it needs to evaluate the policy’s performance. The agent spawns 500 concurrent sandboxes, each running an evaluation episode with the updated policy. Each sandbox collects rewards independently, and the agent aggregates all rewards to provide feedback to the external training system.

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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.

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Example (TypeScript)

import SandboxSDK from '@avmcodes/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,
  };
}

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Next Steps

• Integrate with external GPU-based training systems (PyTorch, TensorFlow)
• Add adaptive evaluation scaling based on policy variance
• Implement reward aggregation and statistical analysis
• Build evaluation result caching for faster iteration