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.

Inverted Pendulum DDPG - single worker

python3 coach.py -p Mujoco_DDPG -lvl inverted_pendulum

Inverted Double Pendulum DDPG - single worker

python3 coach.py -p Mujoco_DDPG -lvl inverted_double_pendulum

Reacher DDPG - single worker

python3 coach.py -p Mujoco_DDPG -lvl reacher

Hopper DDPG - single worker

python3 coach.py -p Mujoco_DDPG -lvl hopper

Half Cheetah DDPG - single worker

python3 coach.py -p Mujoco_DDPG -lvl half_cheetah

Walker 2D DDPG - single worker

python3 coach.py -p Mujoco_DDPG -lvl walker2d

Ant DDPG - single worker

python3 coach.py -p Mujoco_DDPG -lvl ant

Swimmer DDPG - single worker

python3 coach.py -p Mujoco_DDPG -lvl swimmer

Humanoid DDPG - single worker

python3 coach.py -p Mujoco_DDPG -lvl humanoid