You can deploy this model and start earning money today!

Wenhao0/
T2MIR

In-Context RL

reinforcement-learning

2506.05426

mit

Views

No views yet

Mixture-of-Experts Meets In-Context Reinforcement Learning

Sources

Repository: Github
Paper: Mixture-of-Experts Meets In-Context Reinforcement Learning

Model Description

Checkpoints of T2MIR-AD and T2MIR-DPT on Cheetah-Vel using mixed datasets.
To use the provided checkpoints, you will need to rename the file to a specific step (e.g., "89000.pt") and place it into the appropriate directory (e.g., runs/HalfCheetahVel-v0/mixed/horizon-4/exp_0-seed_3407/checkpoints/).

Usage

The provided code implements the T2MIR framework. See the GitHub repository for training and evaluation instructions. Examples are provided below for training and evaluating T2MIR-AD and T2MIR-DPT on the Cheetah-Vel environment.

Training

To train T2MIR-AD, use:

bash

cd T2MIR-AD
python train.py cheetah-vel-v0 --exp exp_0 --seed 3407

To train T2MIR-DPT, use:

bash

cd T2MIR-DPT
python train.py cheetah-vel-v0 --exp exp_0 --seed 3407

Evaluation

To evaluate T2MIR-AD, use:

bash

cd T2MIR-AD
python eval.py cheetah-vel-v0 1 --exp exp_0 --seed 3407 --start-ckpt 89000 --stop-ckpt 89000 --seed-eval 10

To evaluate T2MIR-DPT, use:

bash

cd T2MIR-DPT
python eval_online.py cheetah-vel-v0 1 --exp exp_0 --seed 3407 --start-ckpt 59000 --stop-ckpt 59000 --seed-eval 10

Wenhao0/T2MIR

Mixture-of-Experts Meets In-Context Reinforcement Learning

Sources

Model Description

Usage

Wenhao0/
T2MIR