LARS12llt's repositories
adversarial-surprise
Explore and Control with Adversarial Surprise
brax
Massively parallel rigidbody physics simulation on accelerator hardware.
CollaQ
A code implementation for our arXiv paper "Multi-agent Adhoc Team Play using Decompositional Q function"
distributedRL
A framework for easy prototyping of distributed reinforcement learning algorithms
dqn_zoo
DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.
dreamerv2
Mastering Atari with Discrete World Models
DRIML
Code for Deep Reinforcement and InfoMax Learning (Neurips 2020)
EfficientZero
Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.
google-research
Google Research
h-baselines
A repository of high-performing hierarchical reinforcement learning models and algorithms.
hopfield-layers
Hopfield Networks is All You Need
jax-rl
Jax (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
level-replay
This code implements Prioritized Level Replay, a method for sampling training levels for reinforcement learning agents that exploits the fact that not all levels are equally useful for agents to learn from during training.
mrcl
Code for the NeurIPS19 paper "Meta-Learning Representations for Continual Learning"
muzero
A clean implementation of MuZero and AlphaZero following the AlphaZero General framework. Train and Pit both algorithms against each other, and investigate reliability of learned MuZero MDP models.
muzero-general
MuZero
procgen-competition
Sample efficiency and generalisation in reinforcement learning using procedural generation.
pytorch-a2c-ppo-acktr-gail
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
pytorch_sac_ae
PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)
rad
RAD: Reinforcement Learning with Augmented Data
RE3
RE3: State Entropy Maximization with Random Encoders for Efficient Exploration
rlpyt
Reinforcement Learning in PyTorch
seed_rl
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
Testing
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
VE-principle-for-model-based-RL
Repository for ML Reproducibility Challenge 2020 for the Neurips paper, "The Value Equivalence Principle for Model-Based Reinforcement Learning"
xagents
Train, tune, and use reinforcement learning agents within minutes directly or through command line.