The Best 42 Physics Model Tools in 2025

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.

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

This is a PPO agent model trained using the Unity ML-Agents library, specifically designed for the Walker environment.

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 designed to solve the landing task in the LunarLander-v2 environment.

A sequence-to-sequence Transformer model based on the BART architecture, specifically designed for generating Lagrangians in particle physics.

This is a reinforcement learning model based on the PPO algorithm, specifically designed to solve control problems in the Pendulum-v1 environment.

This is a reinforcement learning model based on the PPO algorithm, specifically designed for the HalfCheetah-v3 environment and trained using the stable-baselines3 library.

This is a reinforcement learning model based on the SAC algorithm, designed to control robot hopping movements in the Hopper-v3 environment.

This is a reinforcement learning model based on the SAC algorithm, specifically designed for the Walker2d-v3 environment to control bipedal robot walking.

Vintix is a multi-task action model achieved through contextual reinforcement learning, demonstrating outstanding performance across multiple benchmarks.

This is a reinforcement learning agent based on the PPO algorithm, trained in the pyramid environment using Unity ML-Agents.

This is a reinforcement learning model based on the SAC algorithm, designed to solve control problems in the Pendulum-v1 environment.

This is a TD3 agent model trained using the stable-baselines3 library, specifically designed for reinforcement learning tasks in the Hopper-v3 environment.

This is a reinforcement learning model based on the Decision Transformer architecture, specifically trained for the Gym HalfCheetah continuous control environment using medium-quality trajectory data.

This is a TD3 reinforcement learning agent trained using the stable-baselines3 library, specifically designed for the HalfCheetah-v3 environment, achieving an average reward of 9709.01.

This is a reinforcement learning agent based on the PPO algorithm, specifically trained for the Unity ML-Agents pyramid environment

This model is designed for time series forecasting, supports the English language, and uses a specific license (bethana15).

This is a deep reinforcement learning model based on the PPO algorithm, specifically designed to solve control problems in the MountainCar-v0 environment.

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

This is a PPO reinforcement learning model trained based on the stable-baselines3 library, specifically designed for continuous control tasks in the Hopper-v3 environment.

This is a reinforcement learning agent based on Deep Q-Network (DQN), specifically trained to solve control problems in the MountainCar-v0 environment.

This is a reinforcement learning model based on the PPO algorithm, designed to solve control problems in the Pendulum-v1 environment.

This is a reinforcement learning agent based on Deep Q-Network (DQN), specifically designed to solve tasks in the MountainCar-v0 environment.

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

This is a reinforcement learning agent based on the PPO algorithm, specifically trained for the LunarLanderContinuous-v2 environment, capable of controlling the lunar lander for smooth landing.

This is a trained decision transformer model, with training data derived from expert trajectories sampled in the Gym Walker2d environment.

This is a PPO agent model trained using the stable-baselines3 library, specifically designed to solve the CartPole-v1 reinforcement learning task.

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

This is a reinforcement learning agent based on the PPO algorithm, trained in Unity's ML-Agents pyramid environment

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 agent based on the PPO algorithm, specifically trained for navigation and task-solving in Unity's ML-Agents pyramid environment.

This is a reinforcement learning agent based on the PPO algorithm, specifically trained for the seals/Walker2d-v0 environment to control the walking task of the Walker2d robot.