Adds action clipping to rsl-rl wrapper #2019
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kellyguo11
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Mar 10, 2025
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jtigue-bdai
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# Description Currently, the actions from the policy are directly applied to the environment and also often fed back to the policy using the last action as observation. Doing this can lead to instability during training since applying a large action can introduce a negative feedback loop. More specifically, applying a very large action leads to a large last_action observations, which often results in a large error in the critic, which can lead to even larger actions being sampled in the future. This PR aims to fix this for RSL-RL library, by clipping the actions to (large) hard limits before applying them to the environment. This prohibits the actions from growing continuously and greatly improves training stability. Fixes #984, #1732, #1999 ## Type of change - Bug fix (non-breaking change which fixes an issue) - New feature (non-breaking change which adds functionality) ## Checklist - [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with `./isaaclab.sh --format` - [x] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [ ] I have added tests that prove my fix is effective or that my feature works - [x] I have updated the changelog and the corresponding version in the extension's `config/extension.toml` file - [x] I have added my name to the `CONTRIBUTORS.md` or my name already exists there
ToxicNS
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Apr 24, 2025
# Description Currently, the actions from the policy are directly applied to the environment and also often fed back to the policy using the last action as observation. Doing this can lead to instability during training since applying a large action can introduce a negative feedback loop. More specifically, applying a very large action leads to a large last_action observations, which often results in a large error in the critic, which can lead to even larger actions being sampled in the future. This PR aims to fix this for RSL-RL library, by clipping the actions to (large) hard limits before applying them to the environment. This prohibits the actions from growing continuously and greatly improves training stability. Fixes isaac-sim#984, isaac-sim#1732, isaac-sim#1999 ## Type of change - Bug fix (non-breaking change which fixes an issue) - New feature (non-breaking change which adds functionality) ## Checklist - [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with `./isaaclab.sh --format` - [x] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [ ] I have added tests that prove my fix is effective or that my feature works - [x] I have updated the changelog and the corresponding version in the extension's `config/extension.toml` file - [x] I have added my name to the `CONTRIBUTORS.md` or my name already exists there
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Description
Currently, the actions from the policy are directly applied to the environment and also often fed back to the policy using the last action as observation.
Doing this can lead to instability during training since applying a large action can introduce a negative feedback loop.
More specifically, applying a very large action leads to a large last_action observations, which often results in a large error in the critic, which can lead to even larger actions being sampled in the future.
This PR aims to fix this for RSL-RL library, by clipping the actions to (large) hard limits before applying them to the environment. This prohibits the actions from growing continuously and greatly improves training stability.
Fixes #984, #1732, #1999
Type of change
Checklist
pre-commitchecks with./isaaclab.sh --formatconfig/extension.tomlfileCONTRIBUTORS.mdor my name already exists there