mirror of
https://github.com/wassname/pytorch-a2c-ppo-acktr.git
synced 2026-06-26 16:10:05 +08:00
Move arguments to a separate file
This commit is contained in:
Ilya Kostrikov
@@ -0,0 +1,61 @@
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import argparse
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import torch
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def get_args():
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parser = argparse.ArgumentParser(description='RL')
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parser.add_argument('--algo', default='a2c',
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help='algorithm to use: a2c | ppo | acktr')
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parser.add_argument('--lr', type=float, default=7e-4,
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help='learning rate (default: 7e-4)')
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parser.add_argument('--eps', type=float, default=1e-5,
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help='RMSprop optimizer epsilon (default: 1e-5)')
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parser.add_argument('--alpha', type=float, default=0.99,
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help='RMSprop optimizer apha (default: 0.99)')
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parser.add_argument('--gamma', type=float, default=0.99,
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help='discount factor for rewards (default: 0.99)')
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parser.add_argument('--use-gae', action='store_true', default=False,
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help='use generalized advantage estimation')
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parser.add_argument('--tau', type=float, default=0.95,
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help='gae parameter (default: 0.95)')
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parser.add_argument('--entropy-coef', type=float, default=0.01,
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help='entropy term coefficient (default: 0.01)')
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parser.add_argument('--value-loss-coef', type=float, default=0.5,
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help='value loss coefficient (default: 0.5)')
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parser.add_argument('--max-grad-norm', type=float, default=0.5,
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help='value loss coefficient (default: 0.5)')
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parser.add_argument('--seed', type=int, default=1,
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help='random seed (default: 1)')
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parser.add_argument('--num-processes', type=int, default=16,
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help='how many training CPU processes to use (default: 16)')
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parser.add_argument('--num-steps', type=int, default=5,
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help='number of forward steps in A2C (default: 5)')
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parser.add_argument('--ppo-epoch', type=int, default=4,
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help='number of ppo epochs (default: 4)')
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parser.add_argument('--batch-size', type=int, default=64,
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help='ppo batch size (default: 64)')
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parser.add_argument('--clip-param', type=float, default=0.2,
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help='ppo clip parameter (default: 0.2)')
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parser.add_argument('--num-stack', type=int, default=4,
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help='number of frames to stack (default: 4)')
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parser.add_argument('--log-interval', type=int, default=10,
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help='log interval, one log per n updates (default: 10)')
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parser.add_argument('--vis-interval', type=int, default=100,
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help='vis interval, one log per n updates (default: 100)')
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parser.add_argument('--num-frames', type=int, default=10e6,
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help='number of frames to train (default: 10e6)')
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parser.add_argument('--env-name', default='PongNoFrameskip-v4',
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help='environment to train on (default: PongNoFrameskip-v4)')
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parser.add_argument('--log-dir', default='/tmp/gym/',
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help='directory to save agent logs (default: /tmp/gym)')
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parser.add_argument('--no-cuda', action='store_true', default=False,
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help='disables CUDA training')
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parser.add_argument('--no-vis', action='store_true', default=False,
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help='disables visdom visualization')
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args = parser.parse_args()
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args.cuda = not args.no_cuda and torch.cuda.is_available()
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args.vis = not args.no_vis
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return args
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@@ -1,4 +1,3 @@
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import argparse
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import glob
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import os
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@@ -11,70 +10,18 @@ import torch.optim as optim
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from torch.autograd import Variable
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from torch.utils.data.sampler import BatchSampler, SubsetRandomSampler
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from arguments import get_args
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from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
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from envs import make_env
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from kfac import KFACOptimizer
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from model import ActorCritic
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from vizualize_atari import visdom_plot
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parser = argparse.ArgumentParser(description='RL')
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parser.add_argument('--algo', default='a2c',
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help='algorithm to use: a2c | ppo | acktr')
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parser.add_argument('--lr', type=float, default=7e-4,
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help='learning rate (default: 7e-4)')
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parser.add_argument('--eps', type=float, default=1e-5,
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help='RMSprop optimizer epsilon (default: 1e-5)')
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parser.add_argument('--alpha', type=float, default=0.99,
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help='RMSprop optimizer apha (default: 0.99)')
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parser.add_argument('--gamma', type=float, default=0.99,
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help='discount factor for rewards (default: 0.99)')
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parser.add_argument('--use-gae', action='store_true', default=False,
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help='use generalized advantage estimation')
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parser.add_argument('--tau', type=float, default=0.95,
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help='gae parameter (default: 0.95)')
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parser.add_argument('--entropy-coef', type=float, default=0.01,
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help='entropy term coefficient (default: 0.01)')
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parser.add_argument('--value-loss-coef', type=float, default=0.5,
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help='value loss coefficient (default: 0.5)')
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parser.add_argument('--max-grad-norm', type=float, default=0.5,
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help='value loss coefficient (default: 0.5)')
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parser.add_argument('--seed', type=int, default=1,
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help='random seed (default: 1)')
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parser.add_argument('--num-processes', type=int, default=16,
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help='how many training CPU processes to use (default: 16)')
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parser.add_argument('--num-steps', type=int, default=5,
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help='number of forward steps in A2C (default: 5)')
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parser.add_argument('--ppo-epoch', type=int, default=4,
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help='number of ppo epochs (default: 4)')
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parser.add_argument('--batch-size', type=int, default=64,
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help='ppo batch size (default: 64)')
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parser.add_argument('--clip-param', type=float, default=0.2,
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help='ppo clip parameter (default: 0.2)')
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parser.add_argument('--num-stack', type=int, default=4,
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help='number of frames to stack (default: 4)')
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parser.add_argument('--log-interval', type=int, default=10,
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help='log interval, one log per n updates (default: 10)')
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parser.add_argument('--vis-interval', type=int, default=100,
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help='vis interval, one log per n updates (default: 100)')
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parser.add_argument('--num-frames', type=int, default=10e6,
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help='number of frames to train (default: 10e6)')
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parser.add_argument('--env-name', default='PongNoFrameskip-v4',
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help='environment to train on (default: PongNoFrameskip-v4)')
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parser.add_argument('--log-dir', default='/tmp/gym/',
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help='directory to save agent logs (default: /tmp/gym)')
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parser.add_argument('--no-cuda', action='store_true', default=False,
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help='disables CUDA training')
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parser.add_argument('--no-vis', action='store_true', default=False,
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help='disables visdom visualization')
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args = parser.parse_args()
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args = get_args()
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assert args.algo in ['a2c', 'ppo', 'acktr']
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if args.algo == 'ppo':
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assert args.num_processes * args.num_steps % args.batch_size == 0
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args.cuda = not args.no_cuda and torch.cuda.is_available()
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args.vis = not args.no_vis
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num_updates = int(args.num_frames) // args.num_steps // args.num_processes
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