TF-AgentsTensorflow
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Related Products
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About
The Agent Client Protocol (ACP) standardizes communication between code editors, IDEs, and coding agents, making agent-editor interoperability the default instead of requiring custom integrations for every possible combination. It provides a standard interface for communication between AI agents and client applications, with a flexible, extensible, and platform-agnostic architecture designed for both local and remote scenarios. ACP addresses integration overhead, limited compatibility, and developer lock-in by allowing agents that implement the protocol to work with any compatible editor, while editors that support ACP gain access to the broader ecosystem of ACP-compatible agents. Similar in spirit to how the Language Server Protocol standardized language server integration, ACP decouples agents and editors so both sides can innovate independently while developers choose the best tools for their workflow.
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About
TensorFlow Agents (TF-Agents) is a comprehensive library designed for reinforcement learning in TensorFlow. It simplifies the design, implementation, and testing of new RL algorithms by providing well-tested modular components that can be modified and extended. TF-Agents enables fast code iteration with good test integration and benchmarking. It includes a variety of agents such as DQN, PPO, REINFORCE, SAC, and TD3, each with their respective networks and policies. It also offers tools for building custom environments, policies, and networks, facilitating the creation of complex RL pipelines. TF-Agents supports both Python and TensorFlow environments, allowing for flexibility in development and deployment. It is compatible with TensorFlow 2.x and provides tutorials and guides to help users get started with training agents on standard environments like CartPole.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Coding agent developers and IDE teams that need a standard protocol to connect AI agents with editors while preserving flexibility, tool access, and user control
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Audience
Academic researchers searching for a tool to develop and test new reinforcement learning algorithms within the TensorFlow ecosystem
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationAgent Client Protocol (ACP)
United States
agentclientprotocol.com/get-started/introduction
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Company InformationTensorflow
Founded: 2015
United States
www.tensorflow.org/agents
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Categories |
Categories |
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Integrations
Python
Grok Build 0.1
HTML
JSON
Java
Kotlin
Markdown
Model Context Protocol (MCP)
Rust
TensorFlow
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Integrations
Python
Grok Build 0.1
HTML
JSON
Java
Kotlin
Markdown
Model Context Protocol (MCP)
Rust
TensorFlow
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