# Reinforcement Learning Control

This is a reinforcement learning model based on Deep Q-Network (DQN), specifically designed to solve the balancing pole problem in the CartPole-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 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 model based on the PPO algorithm, specifically designed for the HalfCheetah-v3 environment and trained using the stable-baselines3 library.

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 DQN agent model trained using stable-baselines3, specifically designed to solve reinforcement learning tasks in the MountainCar-v0 environment.

This is a reinforcement learning model based on the PPO algorithm, specifically designed to solve the balancing problem in the CartPole-v1 environment.

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

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

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