# DDPG Each experiment uses 3 seeds and is trained for 2k environment steps. The parameters used for DDPG are the same parameters as described in the [original paper](https://arxiv.org/abs/1509.02971). ### Inverted Pendulum DDPG - single worker ```bash python3 coach.py -p Mujoco_DDPG -lvl inverted_pendulum ``` Inverted Pendulum DDPG ### Inverted Double Pendulum DDPG - single worker ```bash python3 coach.py -p Mujoco_DDPG -lvl inverted_double_pendulum ``` Inverted Double Pendulum DDPG ### Reacher DDPG - single worker ```bash python3 coach.py -p Mujoco_DDPG -lvl reacher ``` Reacher DDPG ### Hopper DDPG - single worker ```bash python3 coach.py -p Mujoco_DDPG -lvl hopper ``` Hopper DDPG ### Half Cheetah DDPG - single worker ```bash python3 coach.py -p Mujoco_DDPG -lvl half_cheetah ``` Half Cheetah DDPG ### Walker 2D DDPG - single worker ```bash python3 coach.py -p Mujoco_DDPG -lvl walker2d ``` Walker 2D DDPG ### Ant DDPG - single worker ```bash python3 coach.py -p Mujoco_DDPG -lvl ant ``` Ant DDPG ### Swimmer DDPG - single worker ```bash python3 coach.py -p Mujoco_DDPG -lvl swimmer ``` Swimmer DDPG ### Humanoid DDPG - single worker ```bash python3 coach.py -p Mujoco_DDPG -lvl humanoid ``` Humanoid DDPG