FFNN

This is a decision transformer model trained on medium-performance trajectories in the Gym Hopper environment, suitable for continuous control tasks.

This is a trained decision transformer model, with training data sourced from expert trajectories in the Gym Hopper environment.

This is a reinforcement learning agent based on the PPO algorithm, specifically trained for the MountainCarContinuous-v0 environment, capable of effectively solving the continuous control problem of the mountain car.

This is a DQN reinforcement learning agent trained using the stable-baselines3 library, specifically designed to solve the Acrobot-v1 control problem.

A deep learning model for solving the inverse problem of Burgers equation, capable of predicting velocity evolution and estimating physical parameters

LWM 1.1 is an upgraded pre-trained model specifically designed for wireless channel feature extraction, supporting diverse channel configurations to enhance feature extraction quality and generalization capabilities.

A TD3 reinforcement learning agent trained based on the stable-baselines3 library, specifically designed for the MountainCarContinuous-v0 environment.

LWM is the first foundational model in the field of wireless communications, developed as a universal feature extractor capable of extracting fine-grained representations from wireless channel data.

This is a reinforcement learning model based on the PPO algorithm, specifically designed to solve the landing task in the LunarLander-v2 environment.

This is a reinforcement learning model based on the PPO algorithm, specifically trained for the LunarLander-v2 environment to safely control the lunar lander.