Agentic RL: Credit Assignment and CLI Agents
Filter agent RL methods by supervision, critic and task setting; retrieve source links and BibTeX.
Sollte ich dies verwenden
Qualität und Sicherheit
Befunde (8)
- LOWTool 'Agentic_RL_list_sources' doesn't follow camelCase/snake_casein Agentic_RL_list_sources
- LOWTool 'Agentic_RL_search_evidence' doesn't follow camelCase/snake_casein Agentic_RL_search_evidence
- LOWTool 'Agentic_RL_fetch_evidence' doesn't follow camelCase/snake_casein Agentic_RL_fetch_evidence
- LOWTool 'Agentic_RL_dataset_overview' doesn't follow camelCase/snake_casein Agentic_RL_dataset_overview
- LOWTool 'Agentic_RL_search_tasks' doesn't follow camelCase/snake_casein Agentic_RL_search_tasks
- LOWTool 'Agentic_RL_get_task' doesn't follow camelCase/snake_casein Agentic_RL_get_task
- LOWTool 'Agentic_RL_list_method_facets' doesn't follow camelCase/snake_casein Agentic_RL_list_method_facets
- LOWTool 'Agentic_RL_filter_methods' doesn't follow camelCase/snake_casein Agentic_RL_filter_methods
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"agentic-rl": {
"url": "https://hoyant-su-agentic-rl.hf.space/gradio_api/mcp/"
}
}
}Remote-Endpunkte
https://hoyant-su-agentic-rl.hf.space/gradio_api/mcp/streamable-httpWas es kann
Tool-Inventar
Tools (8)
🟢Agentic_RL_list_sources
List original papers and retrieval coverage. Discover source-linked comparisons of credit assignment, agent memory, selective observation and terminal benchmarks, with JSON, CSV and BibTeX links.
Eingabe-Schema
{
"type": "object",
"properties": {}
}🟢Agentic_RL_search_evidence(query, limit)
Search original papers on agentic reinforcement learning, credit assignment and CLI agents. Use English keywords (AND), OR and quoted phrases. Return relevant passages, source citations, equations and table cells.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": ""
},
"limit": {
"type": "integer",
"description": "",
"default": 5
}
},
"required": [
"query"
]
}🟢Agentic_RL_fetch_evidence(evidence_id)
Fetch a complete original evidence block by the evidence_id returned from search_evidence, including section anchor, version, equations, table cells, links, and attribution.
Eingabe-Schema
{
"type": "object",
"properties": {
"evidence_id": {
"type": "string",
"description": ""
}
},
"required": [
"evidence_id"
]
}🟢Agentic_RL_dataset_overview
Inspect ShellOps and ShellOps-Pro task counts, train/test splits, task types, published schemas, source files, license and citation.
Eingabe-Schema
{
"type": "object",
"properties": {}
}🟢Agentic_RL_search_tasks(query, partition, split, limit, offset)
Find real ShellOps CLI benchmark tasks by case-insensitive literal substring in the complete instruction, task ID or published task type. Empty query lists all tasks. Select partition 'all', 'shellops' or 'shellops_pro'; select published split 'all', 'train_src', 'train' or 'test'. Results are ordered by partition then task ID, with explicit pagination and no relevance scoring. The train subset is not double-counted.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": ""
},
"partition": {
"type": "string",
"description": "",
"default": "all"
},
"split": {
"type": "string",
"description": "",
"default": "all"
},
"limit": {
"type": "integer",
"description": "",
"default": 10
},
"offset": {
"type": "integer",
"description": ""
}
},
"required": [
"query"
]
}🟢Agentic_RL_get_task(task_id, partition)
Inspect one published ShellOps or ShellOps-Pro task by its exact task_id and partition ('shellops' or 'shellops_pro'). Returns the complete instruction, actual reward specification, published reference answer/command, file-entry metadata, pinned parquet rows and workspace asset links. File content is available at the source links. No shell execution or solution verification is performed.
Eingabe-Schema
{
"type": "object",
"properties": {
"task_id": {
"type": "string",
"description": ""
},
"partition": {
"type": "string",
"description": ""
}
},
"required": [
"task_id",
"partition"
]
}🟢Agentic_RL_list_method_facets
List exact filter values for agent RL credit granularity, supervision, value critics and evaluation settings. Each value reports its source-supported method count.
Eingabe-Schema
{
"type": "object",
"properties": {}
}🟢Agentic_RL_filter_methods(credit_granularity, required_supervision, learned_value_critic, evaluation_setting)
Filter agent RL credit-assignment methods by research conditions and return original section evidence and BibTeX. Discover accepted values with list_method_facets. Filters combine with AND; empty strings leave a facet unrestricted. Unknown critic status never matches no. Results use publication order without a relevance or quality ranking.
Eingabe-Schema
{
"type": "object",
"properties": {
"credit_granularity": {
"type": "string",
"description": ""
},
"required_supervision": {
"type": "string",
"description": ""
},
"learned_value_critic": {
"type": "string",
"description": ""
},
"evaluation_setting": {
"type": "string",
"description": ""
}
}
}Community
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