Train and deploy Reinforcement Learning agents faster with SheepRL.
Easy-to-use framework
Run reinforcement learning algorithms with a single simple
instruction.
Accelerated with Lightning Fabric
Thanks to Fabric, we provide a simple, scalable and efficient
framework.
Distributed Training
Run your agents on multiple gpus.
Use different algorithms
You can use one of the already available algorithms, or create your
own.
python sheeprl exp=ppo
exp=ppo env=gym env.id=CartPole-v1
python sheeprl.py exp=ppo
exp=ppo env=gym env.id=CartPole-v1
fabric.devices=2
python sheeprl.py exp=ppo
exp=ppo env=gym env.id=CartPole-v1
fabric.devices=2
fabric.accelerator=gpu
python sheeprl.py
exp=sac env=gym
env.id=LunarLanderContinuous-v2
algo.total_steps=2000000
env.capture_video=True
Monitor your experiments
Visualize your trained agent behaviour
python sheeprl.py exp=ppo
fabric.devices=[2,3]
fabric.accelerator=gpu
algo.cnn_keys.encoder=[rgb]
algo.mlp_keys.encoder=[]
env=atari
env.id=PongNoFrameskip-v4
algo.optimizer.lr=2.5e-4
algo.anneal_lr=True
algo.anneal_ent_coef=True
algo.anneal_clip_coef=True
algo.ent_coef=0.01
algo.clip_coef=0.1
algo.rollout_steps=128
algo.update_epochs=3
env.num_envs=8
algo.per_rank_batch_size=128
algo.total_steps=40000000
env.capture_video=True
python sheeprl.py exp=ppo
fabric.devices=[2,3]
fabric.accelerator=gpu
env=atari
env.id=BreakoutNoFrameskip-v4
algo.cnn_keys.encoder=[rgb]
algo.mlp_keys.encoder=[]
algo.anneal_lr=True
algo.anneal_ent_coef=True
algo.anneal_clip_coef=True
algo.ent_coef=0.01
algo.clip_coef=0.1
algo.rollout_steps=128
algo.optimizer.lr=2.5e-4
algo.update_epochs=3
env.num_envs=8
algo.per_rank_batch_size=128
algo.total_steps=40000000
env.capture_video=True