[Enhancement] - Add new environment - Support Unity ML-Agents-Envs

[AlirezaShamsoshoara]

Add Unity ML-Agents Environment

Summary

This PR adds a new Unity ML-Agents environment wrapper to OpenEnv, providing access to Unity's reinforcement learning environments (PushBlock, 3DBall, GridWorld, etc.) through the standardized OpenEnv HTTP/WebSocket interface.

Features

• Full Unity ML-Agents Integration: Wraps all environments from the ML-Agents default registry (17+ environments)
• Multiple Deployment Modes:
  • Direct Mode: Run Unity environments directly in-process (recommended for local development)
  • Server Mode: Client-server architecture via HTTP/WebSocket
  • Docker Mode: Containerized deployment for production/cloud environments
• Action Space Support: Both discrete (PushBlock, GridWorld) and continuous (3DBall) action spaces
• Dynamic Environment Switching: Switch between environments at runtime without restarting
• Headless Mode: Run without graphics for faster training
• HuggingFace Spaces Ready: Configured for deployment on HuggingFace Spaces

New Files

envs/unity_env/
├── README.md              # Comprehensive documentation
├── pyproject.toml         # Package configuration
├── client.py              # EnvClient implementation
├── models.py              # Action, Observation, State models
├── assets/                # Demo GIFs
│   ├── unity_pushblock.gif
│   └── unity_3dball.gif
└── server/
    ├── Dockerfile         # Docker configuration
    ├── app.py             # FastAPI server
    └── unity_environment.py  # Core environment wrapper

examples/
└── unity_simple.py        # Example usage script

tests/envs/
└── test_unity_environment.py  # Comprehensive test suite (19 tests)

Supported Environments

Environment	Action Type	Description
PushBlock	Discrete (7)	Push a block to a goal position
3DBall	Continuous (2)	Balance a ball on a platform
3DBallHard	Continuous (2)	Harder version of 3DBall
GridWorld	Discrete (5)	Navigate a grid to find goals
Basic	Discrete (3)	Simple left/right movement
+ 12 more	Various	All ML-Agents registry environments

Usage Examples

# Direct mode (simplest)
from envs.unity_env.client import UnityEnv
from envs.unity_env.models import UnityAction

env = UnityEnv.from_direct(no_graphics=True)
result = env.reset(env_id="PushBlock")
action = UnityAction(discrete_actions=[1])  # Move forward
result = env.step(action)
env.close()

# Server mode
with UnityEnv(base_url="http://localhost:8000") as env:
    result = env.reset(env_id="3DBall")
    action = UnityAction(continuous_actions=[0.5, -0.3])
    result = env.step(action)

Known Limitations

• Apple Silicon + Docker: Docker mode does not work on M1/M2/M3/M4 Macs due to Unity's Mono runtime crashing under x86_64 emulation. Use direct mode or server mode instead (documented in README).
• First Run: Downloads ~500MB of Unity binaries on first use (cached for subsequent runs)
• Single Worker: Unity environments are not thread-safe; use workers=1

Test Plan

• All 19 unit tests pass (pytest tests/envs/test_unity_environment.py -v)
• Direct mode tested locally on macOS
• Server mode tested locally
• Docker mode tested on x86_64 Linux (GitHub Actions / cloud VM)
• HuggingFace Spaces deployment tested
• Documentation reviewed

Dependencies

• mlagents-envs (installed from Unity ML-Agents git repository)
• openenv-core[core] (installed from git for latest features)
• fastapi, uvicorn, pydantic, numpy, pillow

[AlirezaShamsoshoara]

The pushed env on HF is available here:
https://huggingface.co/spaces/Crashbandicoote2/unity_env

[Darktex]

Summary

This PR adds a new Unity ML-Agents environment wrapper to OpenEnv, providing access to Unity's reinforcement learning environments (PushBlock, 3DBall, GridWorld, etc.) through the standardized OpenEnv HTTP/WebSocket interface.

Overall Assessment: Well-structured, comprehensive implementation with excellent documentation. A few minor issues to address.

Highlights

• Excellent Documentation: 611-line README with installation options, usage modes, API reference, and troubleshooting
• Proper OpenEnv Pattern: Uses factory pattern correctly with create_app(UnityMLAgentsEnvironment, ...)
• Comprehensive Tests: 19 tests covering core functionality
• Docker Support: Proper Dockerfile with headless mode considerations

Issues

1. Duplicated Fix from PR #286 (MINOR)

This PR includes the same tomli compatibility fix as PR #286. Coordinate to avoid duplication.

2. CI Workflow Changes (IMPORTANT)

The PR modifies .github/workflows/docker-build.yml:

• Changes path filters from src/** to envs/**
• Renames my-env to connect4_env
• Adds custom context for Unity build

Please verify these changes don't break existing builds.

3. HuggingFace Spaces Link

The docs reference https://huggingface.co/spaces/Crashbandicoote2/unity_env - verify this link after deployment.

RFC Alignment

Requirement	Status
Uses create_app() with class factory	✓
Implements reset() → Observation	✓
Implements step(action) → Observation	✓
Has state property	✓
Action/Observation extend base types	✓
Dockerized deployment	✓

Verdict

APPROVE with minor comments - High-quality environment contribution. Issues are minor and can be addressed during merge.

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Reviewed by Claude

[Darktex]

Note: This is an automated review by Claude Code (alignment-reviewer agent), not a human review. The account posting this is shared with the human maintainer.

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Alignment Review Report

PR Summary

This PR adds a comprehensive Unity ML-Agents environment wrapper to OpenEnv, providing access to 17+ Unity RL environments (PushBlock, 3DBall, GridWorld, etc.) through the standardized OpenEnv interface. The implementation includes:

• Full environment wrapper with server/client architecture
• Docker deployment support
• Comprehensive test suite (19 tests)
• Documentation and example scripts
• Multi-mode deployment (direct, server, Docker)

Files changed: 18 files, +2858/-9 lines

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Automated Checks

Code Quality

• Lint: Not run (ruff not available in review environment)
• Debug code: CLEAN - No debug statements found in production code
  • Print statements found are only in docstrings, examples, and documentation (acceptable)
• Tests: 19 comprehensive unit tests provided
• Documentation: Extensive README with usage examples

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Tier 1: Fixes Required

Critical Issues

1. Client-Server Separation Violation

Location: examples/unity_simple.py:359

from envs.unity_env.server.unity_environment import UnityMLAgentsEnvironment

Issue: The example script imports directly from the server directory, violating the client-server separation invariant (INVARIANTS.md #2).

Why this matters: Client code should never import from server/ directory. This creates tight coupling and breaks the architectural boundary between client and server.

Fix:

• For the "direct mode" use case in the example, the pattern should be to use EnvClient.from_direct() or similar factory method that doesn't expose server internals
• OR document this as an advanced/development-only pattern and move it to a separate dev example
• OR use the environment via the client interface even in direct mode

Severity: MUST FIX before merge

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2. README Documentation Contains Server Import

Location: envs/unity_env/README.md:89, 362, 455

from envs.unity_env.server.unity_environment import UnityMLAgentsEnvironment

Issue: Documentation examples show importing from server directory.

Fix: Update documentation to use proper client interface patterns or clearly mark these as internal/development examples.

Severity: MUST FIX before merge

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Minor Issues

3. Python Version Compatibility Fix

Location: src/openenv/cli/_validation.py:15-19

Change: Added fallback to tomli for Python <3.11 (tomllib not available)

Assessment: ✅ GOOD - This is a proper compatibility fix for older Python versions. The PR description mentions Python 3.10.12 is required for ML-Agents.

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Tier 2: Alignment Discussion Points

1. Long Initialization Times & Timeout Configuration

Observation: The Unity environment can take 30-60+ seconds to initialize (downloading ~500MB binaries on first run). The code uses custom timeout configurations:

• message_timeout_s: float = 180.0 (3 minutes) in client
• ping_timeout=120 (2 minutes) in WebSocket connection
• Test fixture waits up to 60 seconds for server health

Principle at stake: User experience & production readiness (PRINCIPLES.md: "Production-readiness from day one")

The concern: While the implementation handles this correctly with appropriate timeouts, the 30-60s initialization time could be surprising in production. The caching strategy (persistent ~/.mlagents-cache) helps, but:

• First deployment on new infrastructure will be slow
• Docker cold starts will download binaries inside container
• Could impact autoscaling scenarios

Questions for maintainer:

1. Should there be a pre-built Docker image with cached binaries?
2. Should the README have a more prominent warning about first-run time?
3. Is there a way to pre-download binaries during Docker build?

Suggested reviewer: @Darktex

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2. Direct Mode Pattern

Observation: The implementation provides three modes:

1. Direct mode: UnityMLAgentsEnvironment instantiated directly (server code)
2. Server mode: Client connects to running server via WebSocket
3. Docker mode: Client auto-starts Docker container

The "direct mode" is actively promoted in the README as "recommended for local development" and uses direct server imports.

Principle at stake: Client-server separation (INVARIANTS.md #2)

The concern: Is "direct mode" an acceptable pattern in OpenEnv? Other environments (echo_env, snake_env) don't prominently feature this pattern. This creates two ways to use environments:

• Via client interface (clean separation)
• Via direct server imports (breaks separation)

Trade-off: Direct mode is convenient for development and avoids server overhead, but it:

• Violates the architectural boundary
• Creates confusion about which pattern to use
• May encourage anti-patterns in user code

Questions for maintainer:

1. Is direct mode acceptable as a development-only pattern?
2. Should it be clearly marked as "advanced/internal" use?
3. Should we have a EnvClient.from_direct() factory that maintains the abstraction?

Suggested reviewer: @Darktex

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3. Apple Silicon / Docker Compatibility

Observation: The README documents that Docker mode does NOT work on Apple Silicon (M1/M2/M3/M4) due to Unity's Mono runtime crashing under x86_64 emulation. Direct mode is recommended instead.

Principle at stake: Container isolation & reproducibility (PRINCIPLES.md: "Container isolation for reproducibility")

The concern: One of OpenEnv's core principles is container isolation. Having a major platform (Apple Silicon, increasingly common in development) where Docker doesn't work undermines this principle.

Assessment: This appears to be an upstream Unity limitation (not fixable in OpenEnv), and the PR handles it well:

• Clear documentation of the limitation
• Alternative modes provided (direct, server)
• Platform-specific guidance in README

Recommendation: ✅ ACCEPTABLE - This is a well-documented limitation with workarounds. Consider adding a warning in the Docker build process that detects Apple Silicon.

Suggested reviewer: @Darktex

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4. Single Worker Limitation

Observation: Unity environments are not thread-safe. The code enforces workers=1 and documents this in multiple places.

Principle at stake: Production readiness & scalability

The concern: Single worker limits scalability. In production with high traffic, this could be a bottleneck.

Assessment: ✅ ACCEPTABLE - This is an upstream Unity limitation, not an OpenEnv issue. The implementation handles it correctly:

• Clearly documented
• Enforced in code comments
• WebSocket session support mitigates this (each connection can have its own env instance)

Note: The environment sets SUPPORTS_CONCURRENT_SESSIONS = False which is correct.

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5. Reward Computation

Observation: Rewards come from Unity environment itself (line 323, 328 in unity_environment.py):

reward = float(terminal_steps[terminal_steps.agent_id[0]].reward)
reward = float(decision_steps[decision_steps.agent_id[0]].reward)

Principle at stake: Rewards inside environment (PRINCIPLES.md, RFC 002)

Assessment: ✅ CORRECT - Rewards are computed by the Unity environment and passed through. No external reward computation. This follows the "rewards inside environment" principle.

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6. Episode Termination / Reset Control

Observation: The environment correctly implements:

• reset() for orchestration (returns new episode)
• step() returns done=True when episode ends
• No MCP tools expose reset/step to agents

Principle at stake: "Agents cannot reset" (INVARIANTS.md #1, PRINCIPLES.md)

Assessment: ✅ CORRECT - No violations found. The Gym-like API is only exposed via WebSocket for orchestration, not to agents via MCP.

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7. Git Dependency for Packages

Observation: pyproject.toml installs packages from git:

"openenv-core[core] @ git+https://github.com/meta-pytorch/OpenEnv.git"
"mlagents-envs @ git+https://github.com/Unity-Technologies/ml-agents.git#subdirectory=ml-agents-envs"

Principle at stake: Stability & reproducibility

The concern: Git dependencies can break if:

• Upstream repo changes/deletes branches
• Network issues during install
• Commit hashes not pinned (subject to upstream changes)

Questions for maintainer:

1. Should these be pinned to specific commit hashes?
2. Is there a plan to use PyPI versions when available?
3. This pattern exists in other envs - is it standard for OpenEnv?

Suggested reviewer: @Darktex

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Tier 3: Positive Observations

Things Done Well ✅

1. Excellent Documentation: The README is comprehensive with:

   • Multiple deployment modes explained
   • Troubleshooting section
   • Platform-specific guidance (Apple Silicon)
   • Clear examples for each mode

2. Comprehensive Testing: 19 unit tests covering:

   • Health endpoints
   • Reset/step functionality
   • Environment switching
   • Action spaces (discrete & continuous)
   • State tracking

3. Type Safety: Proper use of Pydantic models and generics:

   • UnityAction, UnityObservation, UnityState all extend base types
   • Type annotations throughout

4. Error Handling: Good error messages and validation:

   • Environment ID validation
   • Timeout handling for slow initialization
   • Graceful cleanup in __del__ and close()

5. Async Support: Implements reset_async() and step_async() to avoid blocking event loop during slow Unity initialization

6. Caching Strategy: Persistent cache for Unity binaries avoids re-downloading

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Summary

Tier 1 Issues: 2 critical, 0 minor

Critical items to fix before merge:

1. Remove server imports from examples/unity_simple.py (client-server separation violation)
2. Update README to use proper client interface patterns

Tier 2 Issues: 7 alignment discussion points

Items for human review:

1. Long initialization times & production implications
2. "Direct mode" pattern and architectural boundaries
3. Apple Silicon Docker compatibility (well-documented limitation)
4. Single worker limitation (upstream constraint)
5. Reward computation ✅ (correct)
6. Reset/episode control ✅ (correct)
7. Git dependencies for packages

Overall Assessment

This is a high-quality contribution with excellent documentation and comprehensive testing. The core implementation follows OpenEnv patterns correctly:

• ✅ Proper client-server separation (except for examples)
• ✅ Rewards stay inside environment
• ✅ No agent access to reset/simulation controls
• ✅ Type safety with Pydantic
• ✅ WebSocket for communication

Blocking issues: Fix the 2 Tier 1 items (server imports in examples/README).

Recommended next steps:

1. Fix Tier 1 issues (should be quick - just update import patterns)
2. Maintainer discussion on Tier 2 alignment points (especially "direct mode" pattern)
3. Consider pre-building Docker image with cached binaries for faster cold starts

Recommendation: 🟡 APPROVE after Tier 1 fixes (with discussion of Tier 2 points)

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Automated review by Claude Code | Learn more about OpenEnv's agentic workflow

[Darktex]

See Claude's review

Dismissing automated approval due to bug in review bot. The original review either had blank content or approved despite finding blocking issues. Please disregard this approval.

[AlirezaShamsoshoara]

See Claude's review

@Darktex Thanks! Addressed the Tier 1 issues in new commits

[Darktex]

Let's land this one first