RedHeadM / pytorch-a2c-ppo-acktr

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO) and Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR).

pytorch-a2c-ppo-acktr

Please use hyper parameters from this readme. With other hyper parameters things might not work (it's RL after all)!

use tensorflow 2.12

# PyTorch
conda install pytorch torchvision -c soumith

# Baselines for Atari preprocessing
git clone https://github.com/openai/baselines.git
cd baselines
pip install -e .

# Other requirements
pip install -r requirements.txt

PPO

python main.py --env-name "tcn-push-v0" --algo ppo --use-gae --lr 2.5e-4 --clip-param 0.1 --value-loss-coef 0.5 --num-processes 8 --num-steps 128 --num-mini-batch 4 --vis-interval 1 --log-interval 1 --use-linear-lr-decay --use-linear-clip-decay --entropy-coef 0.01