Active inference as Bayes-optimal exploration

Compares active inference to Q-learning and Bayesian RL across stationary and non-stationary environments, emphasizing information-seeking behavior and theoretical optimality.

4 members. Each node is clickable.

Loading graph…

Drawn from 3 sources

The papers/notes whose extracted claims & findings make up this cluster.

Bridges (2)

Other communities that share members with this one — cross-cutting threads or papers that sit at the seam between two themes.

Active inference & agent ecology4 shared
Active inference & free energy minimization3 shared

Claims (2)

Behavior prescribed by active inference dynamics is approximately Bayes-optimal.Process theory outcomes produce normatively sound decision-making.
Reinforcement learning is sufficient for agency.Argument that RL meets the agency indicator.

Findings (2)

Active inference agents engage in information-seeking behavior in reward-free FrozenLake environments, contrasting with Q-learning but similar to Bayesian RL.Empirical demonstration on FrozenLake; shows epistemic value drives exploration absent reward signal.
All three agent types (active inference, Q-learning, Bayesian RL) perform adequately in stationary FrozenLake; only active inference achieves Bayes-optimal behavior in non-stationary settings.Key empirical result validating online planning capability of active inference.