PyTorch implementation of deep reinforcement learning algorithms
This repository contains PyTorch implementations of deep reinforcement learning algorithms. The repository will soon be updated including the PyBullet environments!
The repository's high-level structure is:
├── agents
└── common
├── results
├── data
└── graphs
└── save_model
To train all the different agents on PyBullet environments, follow these steps:
git clone https://github.com/dongminlee94/deep_rl.git
cd deep_rl
python run_bullet.py
For other environments, change the last line to run_cartpole.py
, run_pendulum.py
, run_mujoco.py
.
If you want to change configurations of the agents, follow this step:
python run_bullet.py \
--env=HumanoidDeepMimicWalkBulletEnv-v1 \
--algo=sac-aea \
--phase=train \
--render=False \
--load=None \
--seed=0 \
--iterations=200 \
--steps_per_iter=5000 \
--max_step=1000 \
--tensorboard=True \
--gpu_index=0
To watch all the learned agents on PyBullet environments, follow these steps:
python run_bullet.py \
--env=HumanoidDeepMimicWalkBulletEnv-v1 \
--algo=sac-aea \
--phase=test \
--render=True \
--load=envname_algoname_... \
--seed=0 \
--iterations=200 \
--steps_per_iter=5000 \
--max_step=1000 \
--tensorboard=False \
--gpu_index=0
You should copy the saved model name in save_model/envname_algoname_...
and paste the copied name in envname_algoname_...
. So the saved model will be load.