# Deep Reinforcement Learning

A deep reinforcement learning agent trained with Unity ML-Agents, specifically designed for two-player soccer game scenarios.

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 PPO agent model trained using the Unity ML-Agents library, specifically designed for the Huggy game.

This is a PPO agent model trained using the Unity ML-Agents library, specifically designed to run the Huggy Game.

This is a PPO agent model trained using the Unity ML-Agents library, specifically designed to control the behavior of the virtual dog Huggy.

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 PPO agent model trained using the Unity ML-Agents library, specifically designed for reinforcement learning tasks in the Huggy game.

A deep reinforcement learning model trained with Unity ML-Agents, specifically designed for two-player soccer game scenarios

This is a PPO agent model trained using the Unity ML-Agents library, specifically designed for gaming in pyramid environments.

This is a reinforcement learning agent based on the PPO algorithm, specifically trained for Unity's worm game.

A reinforcement learning agent based on the PPO algorithm, specifically trained to play the Snake game

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

A deep reinforcement learning agent trained using the PPO algorithm for Unity's PushBlock game environment

This is a reinforcement learning agent based on the PPO algorithm, specifically trained to complete tasks in Unity's PushBlock environment.

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

This is a PPO agent model trained using the Unity ML-Agents library, specifically designed for gaming and decision-making in the pyramid environment.

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

This is a PPO agent model trained using the Unity ML-Agents library, specifically designed to run the Pyramid game.

This is a PPO agent model trained based on Unity ML-Agents, specifically designed for the Worm game 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 agent model trained with Unity ML-Agents, specifically designed for the pyramid game 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 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.

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

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

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 model based on the SAC algorithm, designed to solve control problems in the Pendulum-v1 environment.

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

A reinforcement learning agent based on the PPO algorithm, designed to control the balancing ball task in the Unity 3DBall environment

This is a reinforcement learning agent trained with the PPO algorithm, designed to control the balancing ball task in the Unity 3DBall game.

This is a PPO agent model trained using the stable-baselines3 library, specifically designed to play the Atari game SeaquestNoFrameskip-v4.

A deep reinforcement learning model trained using the PPO algorithm in the Atari Breakout environment