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rllib
Here are 29 public repositories matching this topic...
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Oct 23, 2020 - Python
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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Jan 22, 2021 - 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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Nov 25, 2020 - 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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Nov 13, 2020 - 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_stAdaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning
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Nov 17, 2020 - 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
RLlib tutorials for Anyscale
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Aug 30, 2020 - Jupyter Notebook
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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Sep 6, 2019 - Python
NIPS challenge 2018 Prosthetics playground and testing ideas
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Jun 12, 2019 - Python
An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial
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Aug 4, 2020 - Python
My attempt to implement 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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Aug 25, 2020 - Jupyter Notebook
RL robust to deadlocks for the Flatland Challenge 2020.
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Nov 27, 2020 - Jupyter Notebook
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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Nov 13, 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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May 26, 2020 - Jupyter Notebook
thiagopbueno
commented
Aug 8, 2019
Implement parameterized linear and non-linear dynamics model with bimodal Gaussian noise.
RL training for the 6DoF manipulator
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Aug 11, 2020 - 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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Feb 1, 2021 - Python
reinforcement learning alogrithm implement with Ray
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Jan 22, 2021 - Python
A simple example of distributed actors using ray - http://rllib.io/
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May 26, 2019 - Python
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
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
Curriculum learning using rllib on gymEnv "CarRacing"
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Jan 14, 2020 - Python
Reinforcement learning algorithm that blends the N-th order Markov property with abstract MDPs, PPO, and a hybrid model-free/model-based approach.
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Dec 16, 2020 - Python
A collection of Jupiter notebook containing reinforcement-learning-based solutions to various classic problems
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Oct 29, 2020 - Jupyter Notebook
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Describe your feature request
Hi guys,
It would be awesome to add API that has same output as
ray memorycommand.Also, it would be good to add some additional output info for
ray.objects(). For example, node IP, IDs of objects which are created in in-process stores, IDs of objects from remote calls (when remote calls are still being executed).Thanks in advance!