/pytorch-gym

Implementation of the Deep Deterministic Policy Gradient(DDPG) in bullet Gym using pytorch

Primary LanguagePython

DDPG in bullet Gym using pytorch

Overview

This is an implementation of Deep Deterministic Policy Gradient (DDPG) in bullet Gym using PyTorch.

Dependencies

  • Python 3.6.2
  • pytorch 0.2.0
  • gym
  • tensorboardX-1.0
  • pybullet (if you want to train agents for bullet env)

Run

  • here is a simple example to train CartPole with high efficiency:

$ cd base

$ python main.py --debug --discrete --env=CartPole-v0 --vis

  • you can use this to understand usage of each argument:

$ python main.py --help

  • some explanation of important arguments:

--debug: print the reward and some other information

--discrete: if the actions are discrete rather than continuous

--vis: render each action (but it would slow down your training speed)

--cuda: train this task using GPU

--test: testing mode

--resume : load model from the path

DDPG from baselines

# run HalfCheetah-v1 with default parameters
cd ./baselines
python main.py

Contributors