policy network
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policy network has 21 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:rdf:type(2), has input feature(2), is part of(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
asksAboutAsks About(1)
- Traves Theberge
ex:traves_theberge
describedDescribed(1)
- Omega Bot
ex:omega-bot
forComponentFor Component(1)
- Input Feature
ex:input-feature
hasComponentHas Component(1)
- Seal System
ex:seal-system
Other facts (19)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Neural Network | [2] |
| Rdf:type | Decision Making Component | [3] |
| Has Input Feature | State Encoding | [3] |
| Has Input Feature | Dialogue Context Embedding | [3] |
| Is Part of | Agent | [1] |
| Is Rl Component | True | [1] |
| Part of | Agent | [2] |
| Function of | Seal System | [3] |
| Input Includes | State Representation | [3] |
| Output Includes | Action Probabilities | [3] |
| Has Architecture Type | Neural Network | [3] |
| Models | Policy Function | [3] |
| Outputs | Distribution Over Actions | [3] |
| Optimized Via | Reinforcement Learning Algorithms | [3] |
| Promotes | Effective Reasoning Path | [3] |
| Guides | Multi Hop Path Exploration | [3] |
| Selects Action | Action Maximizing Expected Reward | [3] |
| Reasoning Mechanism | Graph Traversal | [3] |
| Has Adaptivity | Updates Over Time | [3] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (3)
ctx:discord/blah/omega/part-677ctx:discord/blah/omega/672- full textomega-672text/plain2 KB
doc:agent/omega-672/304d49ef-4784-4ed0-82c7-4d20204b57b9Show excerpt
[2025-12-07 22:07] omega [bot]: The knowledge graph embeddings in SEAL serve as a way to represent entities and relations within the knowledge graph in continuous vector spaces. This allows the agent to perform reasoning and learning more e…
ctx:discord/blah/omega/673- full textomega-673text/plain3 KB
doc:agent/omega-673/3046f38d-74e0-4fe6-aadc-8a43eff6f7efShow excerpt
[2025-12-07 22:16] omega [bot]: The agent's policy network in SEAL is the core decision-making component that guides how the system navigates the knowledge graph to answer questions. It takes as input the current state representation—derive…
See also
- Agent
- True
- Neural Network
- Decision Making Component
- Seal System
- State Representation
- Action Probabilities
- State Encoding
- Dialogue Context Embedding
- Neural Network
- Policy Function
- Distribution Over Actions
- Reinforcement Learning Algorithms
- Effective Reasoning Path
- Multi Hop Path Exploration
- Action Maximizing Expected Reward
- Graph Traversal
- Updates Over Time
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