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name: code_review_env
version: "2.1.0"
description: >
A code review and security audit RL environment for training AI agents.
The agent identifies bugs, security vulnerabilities, and performance issues
across 7 tasks of increasing difficulty (easy → medium → medium-hard → hard).
Features: PBRS reward shaping, graduated near-miss rewards, flood protection,
CAMRL curriculum with task replay, VL return normalization, GRPO batch endpoint,
diversity/exploration bonuses, and cross-language tasks (Python + JavaScript).
type: space
runtime: fastapi
app: server.app:app
entry_point: server
port: 7860
tasks:
- id: bug-detection
difficulty: easy
language: python
num_issues: 3
max_steps: 15
- id: security-audit
difficulty: medium
language: python
num_issues: 7
max_steps: 20
- id: async-review
difficulty: medium-hard
language: python
num_issues: 6
max_steps: 20
- id: data-pipeline
difficulty: hard
language: python
num_issues: 7
max_steps: 25
- id: comprehensive-review
difficulty: hard
language: python
num_issues: 9
max_steps: 30
- id: api-security
difficulty: hard
language: python
num_issues: 8
max_steps: 25
- id: js-security
difficulty: hard
language: javascript
num_issues: 8
max_steps: 25
reward_design:
terminal: "0.70 * F1 + 0.30 * severity_accuracy"
shaping: "PBRS (Ng et al. 1999): phi(s) = (tp/total_gt) * 0.5"
near_miss: "exponential decay: 0.10 * exp(-0.6 * (line_diff - 2)), requires compatible type"
diversity_bonus: "+0.02 for first TP in a new issue category"
exploration_bonus: "+0.01 for first TP in a new file (multi-file tasks)"
flood_protection: "escalating FP penalty after 3rd false positive"
normalization: "VL Norm (2025): normalized_return = cumulative / steps_used"
training:
grpo_endpoint: "/grpo_batch — group-relative advantages A_i = (r_i - mean) / std"
curriculum: "CAMRL with 20% task replay to prevent forgetting"
rollout: "/trl_rollout — TRL GRPOTrainer compatible batch rollout"
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