Awesome multi agent reinforcement learning

MultiAgent Reinforcement learning for RoboCup Rescue Simulator

In this blog post I want to share some of my highlights from the literature. In order to give this post a little more structure, I decided to group the papers into 5 main categories and selected a winner as well as runner-up.

Without further ado, here is my top 10 DRL papers from Disclaimer : I did not read every DRL paper from which would be quite the challenge. Instead I tried to distill some key narratives as well as stories that excite me. So this is my personal top 10 - let me know if I missed your favorite paper!

Most of pre breakthrough accomplishments of Deep RL e. Partial observability, long time-scales as well vast action spaces remained illusive. I tried to choose the winners for the first category based on the scientific contributions and not only the massive scaling of already existing algorithms. Everyone - with enough compute power - can do PPO with crazy batchsizes. But this is definitely not all there is.

The scientific contributions include a unique version of prioritized fictitious self-play aka The Leaguean autoregressive decomposition of the policy with pointer networks, upgoing policy update UPGO - an evolution of the V-trace Off-Policy Importance Sampling correction for structured action spaces as well as scatter connections a special form of embedding that maintains spatial coherence of the entities in map layer.

Often times science fiction biases our perception towards thinking that ML is an arms race. Low-level dexterity, on the other hand, a capability so natural to us, provides a major challenge for current systems. Or so we thought. Domain Randomization has been proposed to obtain a robust policy.

Instead of training the agent on a single environment with a single set of environment-generating hyperparameters, the agent is trained on a plethora of different configurations.

ADR aims to design a curriculum of environment complexities to maximize learning progress. Astonishingly, this together with a PPO-LSTM-GAE-based policy induces a form of meta-learning that apparently appears to have not yet reached its full capabilities by the time of publishing. But honestly, what is more impressive: In-hand manipulation with crazy reward sparsity or learning a fairly short sequence of symbolic transformations?

My Top 10 Deep RL Papers of 2019

That is impressive. While the previous two projects are exciting show-cases of the potential for DRL, they are ridiculously sample-inefficient. Good thing that there are people working on increasing the sample but not necessarily computational efficiency via hallucinating in a latent space. Traditionally, Model-Based RL has been struggling with learning the dynamics of high-dimensional state spaces.

And these are my two favorite approaches:. Specifically, it overcomes the endorsement of the transition dynamics. Planning may then be done by unrolling the deterministic dynamics model in the latent space given the embedded observation. The authors state that planning in latent space also opens up the application of MCTS in environments with stochastic transitions - pretty exciting if you ask me.

PlaNet 2. Dreamer, on the other hand, provides a principled extension to continuous action spaces that is able to tame long-horizon tasks based on high-dimensional visual inputs.I have read the Cityflow doc multiple times but it comes very short to providing enough guidance to get me started. This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.

A selection of state-of-the-art research materials on decision making and motion planning. Implementation of Multi-Agent Reinforcement Learning algorithm s. This is Multi agent deep reinforcement learning repo which trains an agent to play Tennis. It trains by playing against itself.

Simulating Natural Selection

Implementation of Q-Learning using TD error to navigate a maze avoiding obstacles and a moving enemy. Board-and-card games are those which involve higher level of uncertainty as it includes the probability of getting the right card and the moves made by other players. We look to model such games as Markov Games and find an optimal policy through the Minimax — Q algorithm. This will also be a test for the Minimax — Q algorithm to check how it performs in a situation with multiple goal states.

Add a description, image, and links to the multiagent-reinforcement-learning topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the multiagent-reinforcement-learning topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content. Here are 41 public repositories matching this topic Language: All Filter by language.

Sort options. Star 1. Code Issues Pull requests. Paper list of multi-agent reinforcement learning MARL. Updated Feb 23, Star For deep RL and the future of AI. Updated Jun 5, Python.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

awesome multi agent reinforcement learning

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If you want to contribute to this list, please feel free to send a pull request. Also you can contact daochen. Game AI is focusing on predicting which actions should be taken, based on the current conditions. Generally, most games incorporate some sort of AI, which are usually characters or players in the game.

MultiAgent Reinforcement learning for RoboCup Rescue Simulator

For some popular games such as Starcraft and Dota 2developers have spent years to design and refine the AI to enhance the experience. Numerous studies and achievements have been made to game AI in single-agent environments, where there is a single player in the games. For instance, Deep Q-learning is successfully applied to Atari Games.

awesome multi agent reinforcement learning

Multi-agent environments are more challenging since each player has to reason about the other players' moves. Modern reinforcement learning techniques have boosted multi-agent game AI. InAlphaZero taught itself from scratch and learned to master the games of chess, shogi, and Go.

In more recent years, researchers have made efforts to poker games, such as Libratus and DeepStackachieving expert-level performance in Texas Hold'em. Now researchers keep progressing and achieve human-level AI on Dota 2 and Starcraft 2 with deep reinforcement learning. Perfect information means that each player has access to the same information of the game, e.

Imperfect information refers to the situation where players can not observe the full state of the game. For example, in card games, a player can not observe the hands of the other players.

Imperfect information games are usually considered more challenging with more possibilities. This repository gathers some awesome resources for Game AI on multi-agent learning for both perfect and imperfect information games, including but not limited to, open-source projects, review papers, research papers, conferences, and competitions.

The resources are categorized by games, and the papers are sorted by years. Betting games are one of the most popular form of Poker games. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.Quite a lot of jargon used already in the title.

You will understand every bit of it after reading this article. I will start by building some background first about machine learning, types of machine learning techniques and then delve deep into the Reinforcement learning arena. This is where things will start to get slightly technical but I will try to keep it as simple as possible and provide examples wherever possible. Then I will explain how I applied Reinforcement learning to train a bunch of fire brigades to find buildings that are on fire and extinguish fire in those buildings.

So brace yourself for this exciting journey. We are in an age where we can teach machines how to learn and some machines can even learn on their own. This magical phenomenon is called Machine Learning.

But how do machines learn? They learn by finding patterns in similar data. Think of data as the information you acquire from the world. Broadly, there are three types: Supervised learning, Unsupervised learning and Reinforcement learning. The machine learns from labeled data.

Labeled data consists of unlabeled data with a description, label or name of features in the data. The machine learns from unlabeled data. Unlabeled data consists of data which is either taken from nature or created by human to explore the scientific patterns behind it. Some examples of unlabeled data might include photos, audio recordings, videos, news articles, tweets, x-rays, etc. The main concept is there is no explanation, label, tag, class or name for the features in data.

It is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. Though both supervised and reinforcement learning use mapping between input and output, unlike supervised learning where the feedback provided to the agent is a correct set of actions for performing a task, reinforcement learning uses rewards and punishments as signals for positive and negative behavior.

As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences between data points, in the case of reinforcement learning the goal is to find a suitable action model that would maximize the total cumulative reward of the agent.

The figure below illustrates the action-reward feedback loop of a generic RL model.In other words, the multiple cars running simultaneously on a track can be controlled by different control algorithms - heuristic, reinforcement learning-based, etc.

In the game loop in playGame. Sample results for a DDPG agent learned to drive in traffic are available here. The default cars in TORCS are all programmed heuristic racing agents, which do not serve as good stand-ins for 'traffic'. Hence, using chenyi's code is highly recommended. The multi-agent learning simulator was developed by Abhishek Naikextending ugo-nama-kun 's gym-torcsand yanpanlau 's project under the guidance of Anirban Santara, Balaraman Ravindran, and Bharat Kaul, at Intel Labs.

We believe MADRaS will enable new and veteran researchers in academia and the industry to make the dream of fully autonomous driving a reality. Towards the same, we believe that unlike the closed-source secretive technologies of the big players, this project will enable the community to work towards this goal togetherpooling in thoughts and resources to achieve this dream faster.

Hence, we're highly appreciative of all sorts of contributions, big or small, from fellow researchers and users :.Reinforcement learning RL is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward.

Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Instead the focus is on finding a balance between exploration of uncharted territory and exploitation of current knowledge.


The environment is typically stated in the form of a Markov decision process MDPbecause many reinforcement learning algorithms for this context utilize dynamic programming techniques.

Reinforcement learning, due to its generality, is studied in many other disciplines, such as game theorycontrol theoryoperations researchinformation theorysimulation-based optimizationmulti-agent systemsswarm intelligencestatistics and genetic algorithms.

In the operations research and control literature, reinforcement learning is called approximate dynamic programming, or neuro-dynamic programming. The problems of interest in reinforcement learning have also been studied in the theory of optimal controlwhich is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact computation, and less with learning or approximation, particularly in the absence of a mathematical model of the environment.

In economics and game theoryreinforcement learning may be used to explain how equilibrium may arise under bounded rationality. Basic reinforcement is modeled as a Markov decision process :. Rules are often stochastic.

The observation typically involves the scalar, immediate reward associated with the last transition. In many works, the agent is assumed to observe the current environmental state full observability. If not, the agent has partial observability. Sometimes the set of actions available to the agent is restricted a zero balance cannot be reduced. For example, if the current value of the agent is 3 and the state transition reduces the value by 4, the transition will not be allowed.

A reinforcement learning agent interacts with its environment in discrete time steps. The goal of a reinforcement learning agent is to collect as much reward as possible. The agent can possibly randomly choose any action as a function of the history.

When the agent's performance is compared to that of an agent that acts optimally, the difference in performance gives rise to the notion of regret. In order to act near optimally, the agent must reason about the long term consequences of its actions i.

Thus, reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off.

It has been applied successfully to various problems, including robot controlelevator scheduling, telecommunicationsbackgammoncheckers [3] and Go AlphaGo. Two elements make reinforcement learning powerful: the use of samples to optimize performance and the use of function approximation to deal with large environments. Thanks to these two key components, reinforcement learning can be used in large environments in the following situations:.

The first two of these problems could be considered planning problems since some form of model is availablewhile the last one could be considered to be a genuine learning problem. However, reinforcement learning converts both planning problems to machine learning problems. The exploration vs. Reinforcement learning requires clever exploration mechanisms.

Randomly selecting actions, without reference to an estimated probability distribution, shows poor performance. The case of small finite Markov decision processes is relatively well understood.

However, due to the lack of algorithms that scale well with the number of states or scale to problems with infinite state spacessimple exploration methods are the most practical. Even if the issue of exploration is disregarded and even if the state was observable assumed hereafterthe problem remains to use past experience to find out which actions lead to higher cumulative rewards.

Hence, roughly speaking, the value function estimates "how good" it is to be in a given state. The algorithm must find a policy with maximum expected return. From the theory of MDPs it is known that, without loss of generality, the search can be restricted to the set of so-called stationary policies.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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A curated list of multiagent learning and related area resources. The papers are sorted by algorithms so far. Welcome to send me email jack gmail. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master.

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awesome multi agent reinforcement learning

Latest commit Fetching latest commit…. Awesome Multiagent Learning: A curated list of multiagent learning and related area resources. Contributing Welcome to send me email jack gmail. Multiagent Systems [Website] G. Mesbahi and M. Shoham, K. Bullo, J. Godsil and G. Wang, W.


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