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https://github.com/open-jarvis/OpenJarvis.git
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Jon Saad-FalconandClaude Opus 4.6
8d538cd1b0
Major changes across parallel sessions: - Add orchestrator SFT & GRPO training subpackage (learning/orchestrator/) with episode types, multi-objective reward, prompt registry, policy model, RL environment, and registered learning policies - Add structured THOUGHT/TOOL/INPUT/FINAL_ANSWER mode to OrchestratorAgent - Add 15 channel backends (Discord, Slack, Telegram, Email, Webhook, IRC, Matrix, Teams, WhatsApp, Signal, Mattermost, BlueBubbles, Feishu, Google Chat, Webchat) with channel tools and config - Add LiteLLM engine backend for unified LLM provider access - Add RLM agent and REPL tool - Remove ToolLearningPolicy — learning taxonomy now only targets Intelligence (LM weights/routing) and Agents (logic/ICL/tool strategies) - Rename SFTPolicy to SFTRouterPolicy (backward-compat alias kept) - Remove OpenClaw agent infrastructure (openclaw*.py, openclaw_bridge.py) - Fix async streaming tests (asyncio.run vs deprecated get_event_loop) - Fix server channel route tests (pytest.importorskip for optional fastapi) - Track uv.lock for reproducibility - Update CLAUDE.md and docs to reflect all changes 1676 tests pass, 37 skipped. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
99 lines
2.2 KiB
Markdown
99 lines
2.2 KiB
Markdown
# Learning Module
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The learning module implements learning policies that improve routing, agent,
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and tool decisions based on historical interaction outcomes. The module provides
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a `LearningPolicy` ABC taxonomy with specialized sub-ABCs for intelligence
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(model routing), agent behavior, and tool selection. It also includes reward
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functions for scoring inference results.
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## Abstract Base Classes
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### RouterPolicy
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::: openjarvis.intelligence._stubs.RouterPolicy
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options:
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show_source: true
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members_order: source
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### QueryAnalyzer
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::: openjarvis.intelligence._stubs.QueryAnalyzer
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options:
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show_source: true
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members_order: source
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### RoutingContext
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`RoutingContext` is defined in [`core/types.py`](core.md#openjarvis.core.types.RoutingContext).
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### RewardFunction
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::: openjarvis.learning._stubs.RewardFunction
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options:
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show_source: true
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members_order: source
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### LearningPolicy Taxonomy
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The learning system defines a hierarchy of learning policy ABCs:
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- **`LearningPolicy`** -- base ABC for all learning policies
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- **`IntelligenceLearningPolicy`** -- specialization for model routing decisions
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- **`AgentLearningPolicy`** -- specialization for agent behavior advice (ICL examples, tool-use strategies)
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---
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## Policy Implementations
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### TraceDrivenPolicy
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::: openjarvis.learning.trace_policy.TraceDrivenPolicy
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options:
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show_source: true
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members_order: source
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### classify_query
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::: openjarvis.learning.trace_policy.classify_query
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options:
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show_source: true
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### SFTRouterPolicy
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::: openjarvis.learning.sft_policy.SFTRouterPolicy
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options:
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show_source: true
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members_order: source
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### AgentAdvisorPolicy
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::: openjarvis.learning.agent_advisor.AgentAdvisorPolicy
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options:
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show_source: true
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members_order: source
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### ICLUpdaterPolicy
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::: openjarvis.learning.icl_updater.ICLUpdaterPolicy
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options:
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show_source: true
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members_order: source
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### GRPORouterPolicy
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::: openjarvis.learning.grpo_policy.GRPORouterPolicy
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options:
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show_source: true
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members_order: source
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---
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## Reward Functions
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### HeuristicRewardFunction
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::: openjarvis.learning.heuristic_reward.HeuristicRewardFunction
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options:
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show_source: true
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members_order: source
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