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Resets recurrent state after episode termination in RSL-RL play.py #3838

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Merged
merged 1 commit into from
Nov 3, 2025
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Resets recurrent state after episode termination in RSL-RL play.py #3838

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4 changes: 3 additions & 1 deletion scripts/reinforcement_learning/rsl_rl/play.py
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Expand Up @@ -185,7 +185,9 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
# agent stepping
actions = policy(obs)
# env stepping
obs, _, _, _ = env.step(actions)
obs, _, dones, _ = env.step(actions)
# reset recurrent states for episodes that have terminated
policy_nn.reset(dones)
if args_cli.video:
timestep += 1
# Exit the play loop after recording one video
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2 changes: 1 addition & 1 deletion source/isaaclab/config/extension.toml
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@@ -1,7 +1,7 @@
[package]

# Note: Semantic Versioning is used: https://semver.org/
version = "0.47.5"
version = "0.47.6"

# Description
title = "Isaac Lab framework for Robot Learning"
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10 changes: 10 additions & 0 deletions source/isaaclab/docs/CHANGELOG.rst
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@@ -1,6 +1,16 @@
Changelog
---------


0.47.6 (2025-11-01)
~~~~~~~~~~~~~~~~~~~~

Fixed
^^^^^

* Fixed an issue in recurrent policy evaluation in RSL-RL framework where the recurrent state was not reset after an episode termination.


0.47.5 (2025-10-30)
~~~~~~~~~~~~~~~~~~~

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