Mujoco-Pytorch
- Reinforcement learning algorithm Application on Mujoco
Implemented Algorithm
- PPO
- SAC
- DDPG
Performance
PPO
hopper
reacher
swimmer
walker2d
humanoid
ant
halfcheetah
Requirements
- python 3.6. >=
- gym 0.17. >=
- mujoco_py 2.0. >=
- pytorch 1.6. >=
RUN
python main.py
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if you want to change any options, check "python main.py --help"
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you train and test agent using main.py
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'--env_name', type=str, default = 'Hopper-v2', help = "'Ant-v2','HalfCheetah-v2','Hopper-v2','Humanoid-v2','HumanoidStandup-v2',
'InvertedDoublePendulum-v2', 'InvertedPendulum-v2','Walker2d-v2','Swimmer-v2','Reacher-v2'(default : Hopper-v2)" -
'--train', type=bool, default=True, help="(default: True)"
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'--render', type=bool, default=False, help="(default: False)"
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'--epochs', type=int, default=1000, help='number of epochs, (default: 1000)'
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'--entropy_coef', type=float, default=1e-2, help='entropy coef (default : 0.01)'
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'--critic_coef', type=float, default=0.5, help='critic coef (default : 0.5)'
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'--learning_rate', type=float, default=3e-4, help='learning rate (default : 0.0003)'
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'--gamma', type=float, default=0.99, help='gamma (default : 0.99)'
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'--lmbda', type=float, default=0.95, help='lambda using GAE(default : 0.95)'
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'--eps_clip', type=float, default=0.2, help='actor and critic clip range (default : 0.2)'
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'--K_epoch', type=int, default=64, help='train epoch number(default : 10)'
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'--T_horizon', type=int, default=2048, help='one generation before training(default : 2048)'
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'--hidden_dim', type=int, default=64, help='actor and critic network hidden dimension(default : 64)'
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'--minibatch_size', type=int, default=64, help='minibatch size(default : 64)'
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'--tensorboard', type=bool, default=False, help='use_tensorboard, (default: False)'
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'--load', type=str, default = 'no', help = 'load network name in ./model_weights'
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'--save_interval', type=int, default = 100, help = 'save interval(default: 100)'
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'--print_interval', type=int, default = 20, help = 'print interval(default : 20)'
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'--use_cuda', type=bool, default = True, help = 'cuda usage(default : True)'