Research map / Reinforcement learning / Visual and representation RL
A neurally plausible model learns successor representations in partially observable environments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use inferred latent-state posteriors (DDC distributional successor features) instead of noisy observations as state features | Successor-feature value learning · Noisy 2D box environment with an internal wall | value-function accuracy | Helped |
| Use inferred latent-state posteriors instead of noisy observations as state features | Successor-feature policy learning · Noisy 2D box environment with an internal wall | policy performance | Helped |
Closest papers by meaning
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