Reinforcement Learning (RL)
3929 papers with code • 1 benchmarks • 15 datasets
Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
Libraries
Use these libraries to find Reinforcement Learning (RL) models and implementations
ProcGen
ManiSkill2
PRM800K
SMACv2
V-D4RL
QDax
FinRL-Meta
Avalon
CivRealm
Subtasks
Most implemented papers
Simple random search provides a competitive approach to reinforcement learning
A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions.
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
LantaoYu/SeqGAN
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As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data.
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients.
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
deepmind/scalable_agent
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ICML 2018
In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters.
ParlAI: A Dialog Research Software Platform
facebookresearch/ParlAI
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EMNLP 2017
We introduce ParlAI (pronounced "par-lay"), an open-source software platform for dialog research implemented in Python, available at http://parl. ai.
A Distributional Perspective on Reinforcement Learning
facebookresearch/ReAgent
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ICML 2017
We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning.
World Models
hardmaru/WorldModelsExperiments
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We explore building generative neural network models of popular reinforcement learning environments.
Exploration by Random Network Distillation
openai/random-network-distillation
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ICLR 2019
In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods.
Implicit Quantile Networks for Distributional Reinforcement Learning
In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN.
The StarCraft Multi-Agent Challenge
oxwhirl/pymarl
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In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
