RLcycle (pronounced as "recycle") is a reinforcement learning (RL) agents framework. RLcycle provides ready-made RL agents, as well as reusable components for easy prototyping.
Currently, RLcycle provides:
- DQN + enhancements, Distributional: C51, Quantile Regression, Rainbow-DQN.
- Noisy Networks for parameter space noise
- A2C (data parallel) and A3C (gradient parallel).
- DDPG, both Lillicrap et al. (2015) and Fujimoto et al., (2018) versions.
- Soft Actor Critic with automatic entropy coefficient tuning.
- Prioritized Experience Replay and n-step updates for all off-policy algorithms.
RLcycle uses:
- PyTorch for computations and building and optimizing models.
- Hydra for configuring and building agents.
- Ray for parallelizing learning.
- WandB for logging training and testing.
See below for an introduction and guide to using RLcycle, performance benchmarks, and future plans.
If you have any questions or suggestions, feel free to raise an issue or reach out at cjy2129 [at] columbia [dot] edu!
To install:
conda create --name myenv python=3.6.9 pip
conda activate myenv
git clone https://github.com/cyoon1729/RLcycle.git
cd RLcycle
pip install -U -r requirements.txt
pip install -e .
0. A quick look at Hydra for managing configurations
Let's first take a look at one of the many useful things Hydra can do:"""Instantiating classes from yaml files"""
# in ./examples/rectangle.yaml
shape:
class: examples.shapes.Rectangle
params:
height: 5
width: 4Initializing shapes.Rectangle with hydra as in the above yaml file:
"""Instantiating classes from yaml files"""
# in ./examples/shapes.py
class Rectangle:
def __init__(self, width: float, height: float):
self.width = width
self.height = height
def get_area(self):
return width * height
# in ./examples/main.py
import hydra
from omegaconf import DictConfig
@hydra.main(config_path="./examples/rectangle.yaml")
def main(cfg: DictConfig):
shape = hydra.utils.instantiate(layer_info)
print(shape.__class__.__name__) # 'Rectangle'
print(shape.get_area()) # 20
if __main__ == "__main__":
main()If you would like to know more about Hydra, check their documentation out!
1. Running experiments
Run the run_agent.py file and specify the experiment configuration as below:
python run_agent.py configs=atari/rainbow_dqnAlternatively, you can specify the configuration (yaml) file in metaconfig.yaml.
# in ./metaconfig.yaml
defaults:
- configs=atari/rainbow_dqnTo modify experiment arguments or hyperparameters, you can add the flags as below:
python run_agent.py configs=atari/rainbow_dqn configs.experiment_info.env.name=AlienNoFrameskip-v4
python run_agent.py configs=atari/rainbow_dqn configs.hyper_params.batch_size=64
python run_agent.py configs=pybullet/sac configs.hyper_params.batch_size=642. Building configurations for RLcycle
Let's take `atari/rainbow_dqn.yaml` for example:experiment_info:
experiment_name: Rainbow DQN
agent: rlcycle.dqn_base.agent.DQNBaseAgent
learner: rlcycle.dqn_base.learner.DQNLearner
loss: rlcycle.dqn_base.loss.CategoricalLoss
action_selector: rlcycle.dqn_base.action_selector.CategoricalActionSelector
device: cuda
log_wandb: True
# Environment info
env:
name: "PongNoFrameskip-v4"
is_atari: True
is_discrete: True
frame_stack: True
# Experiment default arguments:
total_num_episodes: 5000
test_interval: 100 # Test every 50 episodes
test_num: 5 # Number of episodes to test during test phase
render_train: False # Render all episode steps during training
render_test: True # Render tests
defaults:
- hyper_params: rainbow
- models: duelingC51under experiment_info, we have the fundamental arguments for running RL experiments: which classes (agent, learner, loss) we want to use, and the gym evironment and experiment configurations.
The defaults points to rlcycle/configs/atari/hyper_params/rainbow.yaml for hyperparameters, and rlcycle/configs/atari/models/duelingC51.yaml for model configurations. Taking a closer look at these files, we have:
hyper_params:
batch_size: 64
replay_buffer_size: 100000
use_per: False
per_alpha: 0.5 # PER alpha value
per_beta: 0.4 # PER beta value
per_beta_max: 1.0
per_beta_total_steps: 300000
# Exploration configs
eps: 1.0 # epsilon-greedy exploration
eps_final: 0.0 # minimum epsilon value for exploration
max_exploration_frame: 100000 # eps = eps_final at most until # steps
# Others
update_starting_point: 40000 # update steps when buffer has # experiences stored
gamma: 0.99
tau: 0.005
q_reg_coeff: 0.0
gradient_clip: 10.0
n_step: 3
train_freq: 4
# Optimizer
learning_rate: 0.0000625 # 0.0003
weight_decay: 0.0
adam_eps: 0.00015a pretty standard organization for RL experiment hyperparameters, and
model:
class: rlcycle.common.models.value.DuelingCategoricalDQN
params:
model_cfg:
state_dim: undefined
action_dim: undefined
num_atoms: 51
v_min: -10
v_max: 10
use_conv: True
use_noisy: True
conv_features:
feature1:
class: rlcycle.common.models.layers.Conv2DLayer
params:
input_size: 4
output_size: 32
kernel_size: 8
stride: 4
activation_fn: relu
feature2:
class: rlcycle.common.models.layers.Conv2DLayer
params:
input_size: 32
output_size: 64
kernel_size: 4
stride: 2
activation_fn: relu
feature3:
class: rlcycle.common.models.layers.Conv2DLayer
params:
input_size: 64
output_size: 64
kernel_size: 3
stride: 1
activation_fn: relu
advantage:
fc1:
class: rlcycle.common.models.layers.FactorizedNoisyLinearLayer
params:
input_size: undefined
output_size: 512
post_activation_fn: relu
fc2:
class: rlcycle.common.models.layers.FactorizedNoisyLinearLayer
params:
input_size: 512
output_size: undefined
post_activation_fn: identity
value:
fc1:
class: rlcycle.common.models.layers.FactorizedNoisyLinearLayer
params:
input_size: undefined
output_size: 512
post_activation_fn: identity
fc2:
class: rlcycle.common.models.layers.FactorizedNoisyLinearLayer
params:
input_size: 512
output_size: 1
post_activation_fn: identitywhere we define the parameters for the model and each of its layers. Note that the fields with values
"undefined" will be defined inside the respective python object. For a more simple model yaml configuration file, take a look at rlcycle/configs/atari/models/dqn.yaml.
Extra (Good to know): How RLcycle instantiates components (models, learners, agents, etc)
Most of the components in RLcycle are instantiated via hydra.utils.instantiate, as illustrated in the section above.
Examples:
- models:
"""in ./configs/lunarlander/models/dqn.yaml"""
model:
class: rlcycle.common.models.value.DQNModel
params:
model_cfg:
state_dim: undefined
action_dim: undefined
fc:
input:
class: rlcycle.common.models.layers.LinearLayer
params:
input_size: undefined
output_size: 128
post_activation_fn: relu
hidden:
hidden1:
class: rlcycle.common.models.layers.LinearLayer
params:
input_size: 128
output_size: 128
post_activation_fn: relu
hidden2:
class: rlcycle.common.models.layers.LinearLayer
params:
input_size: 128
output_size: 128
post_activation_fn: relu
output:
class: rlcycle.common.models.layers.LinearLayer
params:
input_size: 128
output_size: undefined
post_activation_fn: identityBuilding the above model:
"""in ./rlcycle/build.py"""
def build_model(model_cfg: DictConfig, device: torch.device):
"""Build model from DictConfigs via hydra.utils.instantiate()"""
model = hydra.utils.instantiate(model_cfg)
return model.to(device)- learners:
# in ./rlcycle/dqn_base/agent.py
#....
self.learner = build_learner(
self.experiment_info, self.hyper_params, self.model_cfg
)
# ...
# in ./rlcycle/build.py
def build_learner(
experiment_info: DictConfig, hyper_params: DictConfig, model: DictConfig
):
"""Build learner from DictConfigs via hydra.utils.instantiate()"""
learner_cfg = DictConfig(dict())
learner_cfg["class"] = experiment_info.learner
learner_cfg["params"] = dict(
experiment_info=experiment_info,
hyper_params=hyper_params,
model_cfg=model
)
learner = hydra.utils.instantiate(learner_cfg)
return learner- agents:
# in ./tests/test_dqn.py
@hydra.main(config_path="../configs/lunarlander/dqn.yaml", strict=False)
def main(cfg: DictConfig):
agent = build_agent(**cfg)
agent.train()
# in ./rlcycle.build.py
def build_agent(
experiment_info: DictConfig, hyper_params: DictConfig, model: DictConfig
):
"""Build agent from DictConfigs via hydra.utils.instantiate()"""
agent_cfg = DictConfig(dict())
agent_cfg["class"] = experiment_info.agent
agent_cfg["params"] = dict(
experiment_info=experiment_info,
hyper_params=hyper_params,
model_cfg=model
)
agent = hydra.utils.instantiate(agent_cfg)
return agentThis should help you get started with building & running agents in RLcycle!
Hyperparameters were not rigorously tuned; most follow those presented in the original papers, with some modifications to work around memory usage.
Click the dropdown below!
Atari BreakoutNoFrameskip-v4 (Will be ready soon)
PyBullet HalfCheetah-v2 (Will be ready soon)
Below are some things I hope to incorporate to RLcycle:
- TRPO and PPO (medium priority)
- IQN (low priority)
- Compatibility with my distributed RL framework distributedRL. (i.e. Ape-X for all off-policy algorithms). (high priority)
- Special thanks to @Medipixel! A lot of the design choices I've made here are based on what I learned through working on their open source RL framework during my internship there.
- Higgsfield's RL-Adventure Series.
- "Human-level control through deep reinforcement learning." Mnih et al., 2015.
- "Dueling Network Architectures for Deep Reinforcement Learning." Wang et al., 2015.
- "Prioritized Experience Replay." Schaul et al., 2015.
- "Noisy Networks for Exploration." Fortunato et al., 2017.
- "A Distributional Perspective on Reinforcement Learning." Bellemare et al., 2017.
- "Rainbow: Combining Improvements in Deep Reinforcement Learning." Hessel et al., 2017.
- "Distributional Reinforcement Learning with Quantile Regression." Dabney et al., 2017.
- "Asynchronous methods for Deep Reinforcement Learning." Mnih et al., 2016
- "Continuous control with deep reinforcement learning." Lillicrap et al., 2015.
- "Addressing function approximation error in actor-critic methods." Fujimoto et al., 2018
- "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor." Haarnoja et al., 2018
- "Soft Actor-Critic Algorithms and Applications." Haarnoja et al., 2019"
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