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RL Games: High performance RL library

Discord Channel Link

Papers and related links

Some results on the different environments

Ant_running Humanoid_running

Allegro_Hand_400 Shadow_Hand_OpenAI


Implemented in Pytorch:

  • PPO with the support of asymmetric actor-critic variant
  • Support of end-to-end GPU accelerated training pipeline with Isaac Gym and Brax
  • Masked actions support
  • Multi-agent training, decentralized and centralized critic variants
  • Self-play

Implemented in Tensorflow 1.x (was removed in this version):

  • Rainbow DQN
  • A2C
  • PPO

Quickstart: Colab in the Cloud

Explore RL Games quick and easily in colab notebooks:


For maximum training performance a preliminary installation of Pytorch 1.9+ with CUDA 11.1+ is highly recommended:

conda install pytorch torchvision cudatoolkit=11.3 -c pytorch -c nvidia or: pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html


pip install rl-games

To run CPU-based environments either Ray or envpool are required pip install envpool or pip install ray To run Mujoco, Atari games or Box2d based environments training they need to be additionally installed with pip install gym[mujoco], pip install gym[atari] or pip install gym[box2d] respectively.

To run Atari also pip install opencv-python is required. In addition installation of envpool for maximum simulation and training perfromance of Mujoco and Atari environments is highly recommended: pip install envpool


If you use rl-games in your research please use the following citation:

title = {rl-games: A High-performance Framework for Reinforcement Learning},
author = {Makoviichuk, Denys and Makoviychuk, Viktor},
month = {May},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/Denys88/rl_games}},

Development setup

poetry install
# install cuda related dependencies
poetry run pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html


NVIDIA Isaac Gym

Download and follow the installation instructions of Isaac Gym: https://developer.nvidia.com/isaac-gym
And IsaacGymEnvs: NVIDIA-Omniverse/IsaacGymEnvs


python train.py task=Ant headless=True python train.py task=Ant test=True checkpoint=nn/Ant.pth num_envs=100


python train.py task=Humanoid headless=True python train.py task=Humanoid test=True checkpoint=nn/Humanoid.pth num_envs=100

Shadow Hand block orientation task

python train.py task=ShadowHand headless=True python train.py task=ShadowHand test=True checkpoint=nn/ShadowHand.pth num_envs=100


Atari Pong

poetry install -E atari
poetry run python runner.py --train --file rl_games/configs/atari/ppo_pong.yaml
poetry run python runner.py --play --file rl_games/configs/atari/ppo_pong.yaml --checkpoint nn/PongNoFrameskip.pth

Brax Ant

poetry install -E brax
poetry run pip install --upgrade "jax[cuda]==0.3.13" -f https://storage.googleapis.com/jax-releases/jax_releases.html
poetry run python runner.py --train --file rl_games/configs/brax/ppo_ant.yaml
poetry run python runner.py --play --file rl_games/configs/brax/ppo_ant.yaml --checkpoint runs/Ant_brax/nn/Ant_brax.pth

Experiment tracking

rl_games support experiment tracking with Weights and Biases.

poetry install -E atari
poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --track
WANDB_API_KEY=xxxx poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --track
poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --wandb-project-name rl-games-special-test --track
poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --wandb-project-name rl-games-special-test -wandb-entity openrlbenchmark --track

Multi GPU

We use torchrun to orchestrate any multi-gpu runs.

torchrun --standalone --nnodes=1 --nproc_per_node=2 runner.py --train --file rl_games/configs/ppo_cartpole.yaml

Config Parameters

Field Example Value Default Description
seed 8 None Seed for pytorch, numpy etc.
algo Algorithm block.
name a2c_continuous None Algorithm name. Possible values are: sac, a2c_discrete, a2c_continuous
model Model block.
name continuous_a2c_logstd None Possible values: continuous_a2c ( expects sigma to be (0, +inf), continuous_a2c_logstd ( expects sigma to be (-inf, +inf), a2c_discrete, a2c_multi_discrete
network Network description.
name actor_critic Possible values: actor_critic or soft_actor_critic.
separate False Whether use or not separate network with same same architecture for critic. In almost all cases if you normalize value it is better to have it False
space Network space
continuous continuous or discrete
mu_activation None Activation for mu. In almost all cases None works the best, but we may try tanh.
sigma_activation None Activation for sigma. Will be threated as log(sigma) or sigma depending on model.
mu_init Initializer for mu.
name default
sigma_init Initializer for sigma. if you are using logstd model good value is 0.
name const_initializer
val 0
fixed_sigma True If true then sigma vector doesn't depend on input.
cnn Convolution block.
type conv2d Type: right now two types supported: conv2d or conv1d
activation elu activation between conv layers.
initializer Initialier. I took some names from the tensorflow.
name glorot_normal_initializer Initializer name
gain 1.4142 Additional parameter.
convs Convolution layers. Same parameters as we have in torch.
filters 32 Number of filters.
kernel_size 8 Kernel size.
strides 4 Strides
padding 0 Padding
filters 64 Next convolution layer info.
kernel_size 4
strides 2
padding 0
filters 64
kernel_size 3
strides 1
padding 0
mlp MLP Block. Convolution is supported too. See other config examples.
units Array of sizes of the MLP layers, for example: [512, 256, 128]
d2rl False Use d2rl architecture from https://arxiv.org/abs/2010.09163.
activation elu Activations between dense layers.
initializer Initializer.
name default Initializer name.
rnn RNN block.
name lstm RNN Layer name. lstm and gru are supported.
units 256 Number of units.
layers 1 Number of layers
before_mlp False False Apply rnn before mlp block or not.
config RL Config block.
reward_shaper Reward Shaper. Can apply simple transformations.
min_val -1 You can apply min_val, max_val, scale and shift.
scale_value 0.1 1
normalize_advantage True True Normalize Advantage.
gamma 0.995 Reward Discount
tau 0.95 Lambda for GAE. Called tau by mistake long time ago because lambda is keyword in python :(
learning_rate 3e-4 Learning rate.
name walker Name which will be used in tensorboard.
save_best_after 10 How many epochs to wait before start saving checkpoint with best score.
score_to_win 300 If score is >=value then this value training will stop.
grad_norm 1.5 Grad norm. Applied if truncate_grads is True. Good value is in (1.0, 10.0)
entropy_coef 0 Entropy coefficient. Good value for continuous space is 0. For discrete is 0.02
truncate_grads True Apply truncate grads or not. It stabilizes training.
env_name BipedalWalker-v3 Envinronment name.
e_clip 0.2 clip parameter for ppo loss.
clip_value False Apply clip to the value loss. If you are using normalize_value you don't need it.
num_actors 16 Number of running actors/environments.
horizon_length 4096 Horizon length per each actor. Total number of steps will be num_actors*horizon_length * num_agents (if env is not MA num_agents==1).
minibatch_size 8192 Minibatch size. Total number number of steps must be divisible by minibatch size.
minibatch_size_per_env 8 Minibatch size per env. If specified will overwrite total number number the default minibatch size with minibatch_size_per_env * nume_envs value.
mini_epochs 4 Number of miniepochs. Good value is in [1,10]
critic_coef 2 Critic coef. by default critic_loss = critic_coef * 1/2 * MSE.
lr_schedule adaptive None Scheduler type. Could be None, linear or adaptive. Adaptive is the best for continuous control tasks. Learning rate is changed changed every miniepoch
kl_threshold 0.008 KL threshould for adaptive schedule. if KL < kl_threshold/2 lr = lr * 1.5 and opposite.
normalize_input True Apply running mean std for input.
bounds_loss_coef 0.0 Coefficient to the auxiary loss for continuous space.
max_epochs 10000 Maximum number of epochs to run.
max_frames 5000000 Maximum number of frames (env steps) to run.
normalize_value True Use value running mean std normalization.
use_diagnostics True Adds more information into the tensorboard.
value_bootstrap True Bootstraping value when episode is finished. Very useful for different locomotion envs.
bound_loss_type regularisation None Adds aux loss for continuous case. 'regularisation' is the sum of sqaured actions. 'bound' is the sum of actions higher than 1.1.
bounds_loss_coef 0.0005 0 Regularisation coefficient
use_smooth_clamp False Use smooth clamp instead of regular for cliping
zero_rnn_on_done False True If False RNN internal state is not reset (set to 0) when an environment is rest. Could improve training in some cases, for example when domain randomization is on
player Player configuration block.
render True False Render environment
deterministic True True Use deterministic policy ( argmax or mu) or stochastic.
use_vecenv True False Use vecenv to create environment for player
games_num 200 Number of games to run in the player mode.
env_config Env configuration block. It goes directly to the environment. This example was take for my atari wrapper.
skip 4 Number of frames to skip
name BreakoutNoFrameskip-v4 The exact name of an (atari) gym env. An example, depends on the training env this parameters can be different.

Custom network example:

simple test network
This network takes dictionary observation. To register it you can add code in your init.py

from rl_games.envs.test_network import TestNetBuilder 
from rl_games.algos_torch import model_builder
model_builder.register_network('testnet', TestNetBuilder)

simple test environment example environment

Additional environment supported properties and functions

Field Default Value Description
use_central_value False If true than returned obs is expected to be dict with 'obs' and 'state'
value_size 1 Shape of the returned rewards. Network wil support multihead value automatically.
concat_infos False Should default vecenv convert list of dicts to the dicts of lists. Very usefull if you want to use value_boostrapping. in this case you need to always return 'time_outs' : True or False, from the env.
get_number_of_agents(self) 1 Returns number of agents in the environment
has_action_mask(self) False Returns True if environment has invalid actions mask.
get_action_mask(self) None Returns action masks if has_action_mask is true. Good example is SMAC Env

Release Notes

1.6.1 (Unreleased)

  • Fixed Central Value RNN bug which occurs if you train ma multi agent environment.
  • Added Deepmind Control PPO benchmark.
  • Added a few more experimental ways to train value prediction (OneHot, TwoHot encoding and crossentropy loss instead of L2).
  • New methods didn't. It is impossible to turn it on from the yaml files. Once we find an env which trains better it will be added to the config.
  • Added shaped reward graph to the tensorboard.


  • Added ONNX export colab example for discrete and continious action spaces. For continuous case LSTM policy example is provided as well.
  • Improved RNNs training in continuous space, added option zero_rnn_on_done.
  • Added NVIDIA CuLE support: NVlabs/cule
  • Added player config everride. Vecenv is used for inference.
  • Fixed multi-gpu training with central value.
  • Fixed max_frames termination condition, and it's interaction with the linear learning rate: https://github.com/Denys88/rl_games/issues/212
  • Fixed "deterministic" misspelling issue.
  • Fixed Mujoco and Brax SAC configs.
  • Fixed multiagent envs statistics reporting. Fixed Starcraft2 SMAC environments.


  • Added observation normalization to the SAC.
  • Returned back adaptive KL legacy mode.


  • Fixed build package issue.


  • Added wandb support.
  • Added poetry support.
  • Fixed various bugs.
  • Fixed cnn input was not divided by 255 in case of the dictionary obs.
  • Added more envpool mujoco and atari training examples. Some of the results: 15 min Mujoco humanoid training, 2 min atari pong.
  • Added Brax and Mujoco colab training examples.
  • Added 'seed' command line parameter. Will override seed in config in case it's > 0.
  • Deprecated horovod in favor of torch.distributed (#171).


  • Added discord channel https://discord.gg/hnYRq7DsQh :)
  • Added envpool support with a few atari examples. Works 3-4x time faster than ray.
  • Added mujoco results. Much better than openai spinning up ppo results.
  • Added tcnn(NVlabs/tiny-cuda-nn) support. Reduces 5-10% of training time in the IsaacGym envs.
  • Various fixes and improvements.


  • Added 'sigma' command line parameter. Will override sigma for continuous space in case if fixed_sigma is True.


  • Fixed SAC not working


  • Simplified rnn implementation. Works a little bit slower but much more stable.
  • Now central value can be non-rnn if policy is rnn.
  • Removed load_checkpoint from the yaml file. now --checkpoint works for both train and play.


  • Added Swish (SILU) and GELU activations, it can improve Isaac Gym results for some of the envs.
  • Removed tensorflow and made initial cleanup of the old/unused code.
  • Simplified runner.
  • Now networks are created in the algos with load_network method.


  • Fixed crash in a play (test) mode in player, when simulation and rl_devices are not the same.
  • Fixed variuos multi gpu errors.


  • Fixed crash when running single Isaac Gym environment in a play (test) mode.
  • Added config parameter clip_actions for switching off internal action clipping and rescaling


  • Added to pypi: pip install rl-games
  • Added reporting env (sim) step fps, without policy inference. Improved naming.
  • Renames in yaml config for better readability: steps_num to horizon_length amd lr_threshold to kl_threshold


  • Some of the supported envs are not installed with setup.py, you need to manually install them
  • Starting from rl-games 1.1.0 old yaml configs won't be compatible with the new version:
    • steps_num should be changed to horizon_length amd lr_threshold to kl_threshold

Known issues

  • Running a single environment with Isaac Gym can cause crash, if it happens switch to at least 2 environments simulated in parallel
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