[WIP] refactor Action #1
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Reviewer's Guide by SourceryThis PR introduces a refactoring of the Action system by implementing a new structure that integrates with the Gymnasium (formerly OpenAI Gym) framework. The changes establish base classes for actions, action spaces, and environments, particularly focusing on creating a foundation for environment-based interactions. Class diagram for refactored Action systemclassDiagram
class Action {
-name: str
-description: str
-parameters: type[BaseModel]
+run(parameters: dict[str, Any], env: gym.Env | None)
}
class ActionSpace {
-description: str
-action_schema: dict[str, Any]
+to_prompt() str
+to_gym_space() gym.Space
}
class MultiEnvironment {
+__init__()
}
class ActionExecutor {
}
class UbuntuEnvironment {
-name: str
-action_space: Iterator[Action]
-description: str
-action_executor: ActionExecutor | None
+_get_obs() gym.Space
+reset(seed, options)
+step(action)
+render()
}
Action <|-- UbuntuEnvironment
ActionSpace <|-- UbuntuEnvironment
MultiEnvironment <|-- UbuntuEnvironment
ActionExecutor <|-- UbuntuEnvironment
File-Level Changes
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Thanks @2lambda123 for opening this PR! For COLLABORATOR only :
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@2lambda123
Thank you for your contribution to this repository! We appreciate your effort in opening pull request.
Happy coding!
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First PR by @2lambda123 PR Details of @2lambda123 in camel-ai-crab :
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Description has been updated! |
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Caution Review failedThe pull request is closed. WalkthroughThe changes involve updates to the Changes
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@2lambda123
Thank you for your contribution to this repository! We appreciate your effort in closing pull request.
Happy coding!
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| class Action: | ||
| def __init__( | ||
| self, | ||
| name: str, | ||
| description: str, | ||
| parameters: type[BaseModel], | ||
| ): | ||
| pass | ||
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| def run(self, parameters: dict[str, Any], env: gym.Env | None): | ||
| pass | ||
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There was a problem hiding this comment.
The Action class constructor does not store any of the parameters (name, description, parameters) it receives. This could lead to instances of this class not behaving as expected since they don't hold any state related to these inputs.
Recommendation:
Ensure that the constructor initializes and stores these parameters as instance variables. For example:
class Action:
def __init__(self, name: str, description: str, parameters: type[BaseModel]):
self.name = name
self.description = description
self.parameters = parameters| def __init__( | ||
| self, | ||
| name: str, | ||
| action_space: Iterator[Action], |
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The UbuntuEnvironment class is initialized with an action_space parameter typed as an Iterator[Action]. Using an iterator may not be ideal if there is a need to iterate over the actions multiple times within the environment, as iterators in Python are exhausted after one complete iteration.
Recommendation:
Consider changing the type of action_space from Iterator[Action] to a more suitable type like List[Action] or Set[Action] that supports multiple iterations and provides more flexibility in how the actions can be accessed and used.
class UbuntuEnvironment(gym.Env):
def __init__(self, name: str, action_space: List[Action], description: str = "", action_executor: ActionExecutor | None = None, **kwargs: Any) -> None:
# Initialization logic![sourcery-ai[bot]](https://avatars.githubusercontent.com/in/48477?s=60&v=4){kind=small}
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Hey @2lambda123 - I've reviewed your changes and found some issues that need to be addressed.
Blocking issues:
- Missing required Gymnasium environment attributes observation_space and action_space (link)
Overall Comments:
- Please fill out the PR description with details about what this refactoring aims to achieve and what problems it solves.
- The code contains empty method implementations. Please add at least basic implementations or TODO comments explaining the intended behavior for each method.
Here's what I looked at during the review
- 🔴 General issues: 1 blocking issue
- 🟢 Security: all looks good
- 🟢 Testing: all looks good
- 🟡 Complexity: 1 issue found
- 🟢 Documentation: all looks good
Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
| pass | ||
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| class UbuntuEnvironment(gym.Env): |
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issue (bug_risk): Missing required Gymnasium environment attributes observation_space and action_space
Gymnasium environments must define these attributes in init() as per the framework requirements.
| from pydantic import BaseModel | ||
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| class Action: |
There was a problem hiding this comment.
issue (complexity): Consider consolidating the action handling logic into a single ActionSpace class
The current design introduces unnecessary complexity through multiple layers of indirection. Consider simplifying by:
- Merging Action and ActionSpace concepts
- Removing the Iterator[Action] pattern
- Eliminating the separate ActionExecutor
Here's a suggested simplified approach:
class ActionSpace(BaseModel):
description: str
action_schema: dict[str, Any]
def run(self, parameters: dict[str, Any], env: gym.Env | None):
# Implementation moved from Action.run()
pass
def to_prompt(self) -> str:
pass
def to_gym_space(self) -> gym.Space:
pass
class UbuntuEnvironment(gym.Env):
def __init__(
self,
name: str,
action_space: ActionSpace, # Simplified from Iterator[Action]
description: str = "",
**kwargs: Any,
) -> None:
self.action_space = action_space.to_gym_space()
# Rest of initializationThis maintains the validation and description functionality while reducing abstraction layers. The ActionSpace class now directly handles both the gym.Space conversion and action execution, eliminating the need for separate Action instances and executor.
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Description
Related Issue
Types of changes
Checklist:
Summary by Sourcery
Refactor the project to include a new module for action handling and environment management, leveraging the gymnasium library. Update the project dependencies to include gymnasium.
New Features:
Enhancements:
Description by Korbit AI
What change is being made?
Add a new module
demo.pywith classes for action handling, action space definitions, multi-environment setups, and environment management using Gymnasium, also updatepyproject.tomlto include thegymnasiumdependency.Why are these changes being made?
This refactor introduces a structured approach to manage actions and environments in the project, leveraging the Gymnasium package to handle actions and states within a standardized environment framework. This change lays the groundwork for implementing more sophisticated, simulated environments, essential for future enhancements; it replaces outdated or nonexistent foundational code structures with clean, maintainable designs.
Summary by CodeRabbit
New Features
gymnasiumlibrary to enhance reinforcement learning capabilities.Action,ActionSpace,MultiEnvironment, andUbuntuEnvironment.Bug Fixes
Documentation
gymnasium.