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rllib
Here are 36 public repositories matching this topic...
PyBullet Gym environments for single and multi-agent reinforcement learning of quadcopter control
control
reinforcement-learning
uav
quadcopter
robotics
multi-agent
quadrotor
crazyflie
ros2
pybullet
gym-environment
rllib
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Jul 24, 2021 - Python
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Oct 23, 2020 - Python
Deep Reinforcement Learning For Trading
python
machine-learning
reinforcement-learning
deep-learning
trading
keras
jupyter-notebook
quantitative-finance
algorithmic-trading
deep-q-network
quantitative-trading
keras-tensorflow
rllib
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Updated
May 21, 2021 - Jupyter Notebook
A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.
lstm
quantitative-finance
ray
limit-order-book
quantitative-trading
financial-engineering
market-microstructure
zero-sum
high-frequency-trading
gym-environment
ppo
self-play
double-auction
multi-agent-reinforcement-learning
rllib
marl
n-player
zero-sum-games
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Updated
Jun 8, 2021 - Jupyter Notebook
An introductory tutorial about leveraging Ray core features for distributed patterns.
python
distributed-systems
scikit-learn
sharding
profiling
ray
futures
pattern-language
task-parallelism
actor-pattern
rllib
ray-tutorial
introductory-tutorial
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Jul 5, 2021 - Jupyter Notebook
angelolovatto
commented
Sep 12, 2019
Currently we use a very ad-hoc procedure for scaling the quadratic component of NAF when used for exploration:
https://github.com/angelolovatto/raylab/blob/9820275b17ee085e1955a6d845c0bdf61333f8da/raylab/algorithms/naf/naf_policy.py#L150-L155
A possibly better alternative would be to scale it based on the desired average action stddev. Something like:
scale_tril * (1.0 / average_stRLlib tutorials
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May 29, 2021 - Jupyter Notebook
Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning
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Updated
Nov 17, 2020 - Python
An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial
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Jul 7, 2021 - Python
Dynamic multi-cell selection for cooperative multipoint (CoMP) using (multi-agent) deep reinforcement learning
python
mobile
reinforcement-learning
simulation
wireless
cellular
ray
comp
ppo
multi-agent-reinforcement-learning
rllib
cell-selection
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Updated
Jul 28, 2021 - Python
Super Mario Bros training with Ray RLlib DQN algorithm
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May 22, 2021 - Python
Training in bursts for defending against adversarial policies
reinforcement-learning
gym
ray
multiagent-reinforcement-learning
adversarial-examples
population-based-training
rllib
stable-baselines
tensorflow2
adversarial-policies
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Dec 8, 2020 - Python
RL environment replicating the werewolf game to study emergent communication
python
reinforcement-learning
mafia-game
emergent-behavior
werewolf-game
rllib
emergent-communication
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Updated
Jun 8, 2021 - Python
ray project 中文文档
python
java
data-science
machine-learning
reinforcement-learning
deep-learning
optimization
parallel
distributed
model-selection
hyperparameter-optimization
ray
automl
hyperparameter-search
rllib
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Updated
Sep 6, 2019 - Python
NIPS challenge 2018 Prosthetics playground and testing ideas
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Updated
Jun 12, 2019 - Python
My attempt to reproduce a water down version of PBT (Population based training) for MARL (Multi-agent reinforcement learning) using DDPPO (Decentralized & distributed proximal policy optimization) from ray[rllib].
ray
pbt
population-based-training
self-play
multi-agent-reinforcement-learning
rllib
marl
pbt-marl
ddppo
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Updated
Aug 25, 2020 - Jupyter Notebook
Used Flow, Ray/RLlib and OpenAI Gym to simulate and train autonomous vehicles/human drivers in SUMO (Simulation of Urban Mobility)
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Dec 15, 2020 - Jupyter Notebook
RL robust to deadlocks for the Flatland Challenge 2020.
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Nov 27, 2020 - Jupyter Notebook
RL training for the 6DoF manipulator
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Updated
Aug 11, 2020 - Python
An autonomous driving simulator for modelling Vehicle to Infrastructure (V2I) conditions.
reinforcement-learning
newtonian-mechanics
policy-gradient
autonomous-driving
autonomous-vehicles
pygame-application
v2i
gym-environment
ppo
intelligent-driver-model
rllib
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Updated
Mar 25, 2021 - Jupyter Notebook
Sample setup for custom reinforcement learning environment in Sagemaker. This example uses Proximal Policy Optimization with Ray (RLlib).
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Sep 30, 2019 - Python
A simple example of distributed actors using ray - http://rllib.io/
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May 26, 2019 - Python
reinforcement learning alogrithm implement with Ray
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Jul 12, 2021 - Python
A reinforcement learning environment inspired by Among Us
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May 24, 2021 - Python
thiagopbueno
commented
Aug 8, 2019
Implement parameterized linear and non-linear dynamics model with bimodal Gaussian noise.
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Apache Arrow has a first-class tabular file format, Feather, that the Ray Datasets IO layer should support. Combined with Ray Datasets' existing
.from_arrow()and.to_arrow()APIs, this would round out our "all-Arrow" experience, which should be as nice as possible given our "distributed Arrow dataset" positioning.Implementation Note