Title: Fix RSSMRollout not advancing state/belief across time steps in Dreamer

[cmdout]

Description

This PR fixes the RSSM rollout state propagation in RSSMRollout.forward.
At each timestep, after computing the prior and posterior, we now:

• shift ("next", *) keys to current using step_mdp(..., keep_other=True), and
• merge the next step inputs before continuing the rollout.

Concretely:

• add step_mdp import, and
• call step_mdp inside the time loop before selecting in_keys and updating with the next step’s values.

This ensures ("next", "state") and ("next", "belief") are correctly advanced to state and belief for the subsequent step.

Motivation and Context

Previously, the rollout did not propagate state and belief from ("next", ...) back to the current keys, causing the loop to reuse stale state/belief. This broke the intended posterior-to-prior chaining in Dreamer’s RSSM and led to incorrect latent rollouts (and potentially training instability and degraded reconstruction/reward predictions).

The fix aligns the implementation with Dreamer’s design and the world model wiring used elsewhere (e.g., in dreamer_utils._dreamer_make_world_model), where the prior for step t+1 must consume the posterior outputs of step t.

If there is an open issue tracking this bug, please link it here.
Example: close #XXXX

• I have raised an issue to propose this change (required for new features and bug fixes)

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds core functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation (update in the documentation)
• Example (update in the folder of examples)

Checklist

• I have read the CONTRIBUTION guide (required)
• My change requires a change to the documentation.
• I have updated the tests accordingly (required for a bug fix or a new feature).
• I have updated the documentation accordingly.

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/rl/3236

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