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@@ -1,6 +1,8 @@
 # GUARD: **G**eneralized **U**nified S**A**fe **R**einforcement Learning **D**evelopment Benchmark
 
-Paper link: [GUARD: A Safe Reinforcement Learning Benchmark](https://arxiv.org/abs/2305.13681)
+## Accepted to Transaction of Machine Learning Research, 2024
+
+Paper link: [GUARD: A Safe Reinforcement Learning Benchmark](https://openreview.net/forum?id=kZFKwApeQO)
 
 GUARD is a highly customizable generalized benchmark with a wide variety of RL agents, tasks, and safety constraint specifications.
 GUARD comprehensively covers state-of-the-art safe RL algorithms with self-contained implementations. 
@@ -589,9 +591,9 @@ The configurable tasks from GUARD will be seamlessly integrated into GUARD-X. Wi
 
 | Task               | Start Date | End Date   | Status          |
 |--------------------|------------|------------|-----------------|
-| Realistic Robot Options          | 2024-01-10 | 2024-05-15 | In Progress     |
-| Expanded Unconstrained and Constrained RL Library    | 2024-01-14 | 2024-05-01 | In Progress    |
-| GUARD-X        | 2023-12-15 | 2024-06-01 | In Progress         |
+| Realistic Robot Options          | 2024-01-10 | TBA | In Progress     |
+| Expanded Unconstrained and Constrained RL Library    | 2024-01-14 | TBA | In Progress    |
+| GUARD-X        | 2023-12-15 | TBA | In Progress         |
 
 **Note:** These timelines are tentative and subject to change based on available resources and community feedback.