Pytorch implementation of popular deep reinforcement learning algorithms towards SOA performance.
Pytorch implementation of popular deep reinforcement learning algorithms towards SOA performance.
Implemented algorithms:
To be implemented algorithms:
cd ppo
python ppo_train.py --e Reacher-v1 -n 60000 -b 50
python ppo_train.py --e InvertedPendulum-v1
python ppo_train.py --e InvertedDoublePendulum-v1 -n 12000
python ppo_train.py --e Swimmer-v1 -n 2500 -b 5
python ppo_train.py --e Hopper-v1 -n 30000
python ppo_train.py --e HalfCheetah-v1 -n 3000 -b 5
python ppo_train.py --e Walker2d-v1 -n 25000
python ppo_train.py --e Ant-v1 -n 100000
python ppo_train.py --e Humanoid-v1 -n 200000
python ppo_train.py --e HumanoidStandup-v1 -n 200000 -b 5
cd ddpg
python ddpg_train.py --e Reacher-v1 --start_timesteps 1000
python ddpg_train.py --e InvertedPendulum-v1 --start_timesteps 1000
python ddpg_train.py --e InvertedDoublePendulum-v1 --start_timesteps 1000
python ddpg_train.py --e Swimmer-v1 --start_timesteps 1000
python ddpg_train.py --e Hopper-v1 --start_timesteps 1000
python ddpg_train.py --e HalfCheetah-v1 --start_timesteps 10000
python ddpg_train.py --e Walker2d-v1 --start_timesteps 1000
python ddpg_train.py --e Ant-v1 --start_timesteps 10000