advanced
8 min read
Belief States in POMDPs: Acting on Probabilities About Hidden State
A policy that acts on the latest observation alone is guessing. A belief state is how you stop guessing.
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Partial Observability
Decision-making when observations do not reveal the full state, including beliefs, memory, and POMDP reasoning.
Tagged articles
Partial Observability articles
11 articles in this tag.
advanced
8 min read
Communication and Coordination in Multi-Agent RL
More communication is not automatically better coordination. In cooperative multi-agent reinforcement learning, a naive message-passing channel can inject…
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intermediate
12 min read
Markov Decision Processes: The Formal Map Behind Reinforcement Learning
The agent-environment loop feels simple until you try to make an algorithm learn. Then the loop stops being enough. The agent needs to know what it can…
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advanced
9 min read
Multi-Agent Reinforcement Learning Explained: What Changes When Other Agents Learn Too?
Single-agent RL treats the environment as a fixed target. Add a second learning agent, and the target starts moving.
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beginner
10 min read
Partial Observability in Reinforcement Learning: When the Agent Cannot See the Full State
Imagine you are playing a card game against an opponent. You can see your own hand perfectly, but your opponent's cards are hidden. You watch them draw,…
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advanced
10 min read
Derive the Bellman Control Equation Over POMDP Beliefs
You write down the MDP Bellman equation, swap $s$ for $b$, and the result looks plausible. It also predicts nothing you can act on. The symbol changed; the…
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intermediate
12 min read
Deriving the Bayesian Belief Update for a POMDP
The agent never sees the state. It sees a flicker of evidence, acts, and sees another flicker. Yet the entire decision problem collapses into a fixed-size…
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beginner
9 min read
Practice POMDP Belief Updates With a Small Python Example
You can recite the belief update formula and still have no idea whether you are applying it correctly. The way out is not more reading. It is a two-state…
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advanced
7 min read
Recurrent Policies and Memory in Reinforcement Learning
A memoryless policy keeps repeating the same mistake because each observation hides the information it needs. The reflex is to "just add an RNN." The real…
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intermediate
10 min read
Compare Memoryless and Recurrent Policies in a Partially Observed Task
A memoryless agent can learn perfectly and still lose. It extracts every drop of signal from the observation it receives, then plateaus at a score a coin…
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beginner
14 min read
States, Observations, and Actions in Reinforcement Learning
The agent never acts on the world. It acts on a report about the world—and that report is often incomplete, noisy, or misleading. Understanding the gap…
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