dkkim93

9.66_collision_final_project

dkkim93.github.io.old

elas-ros-dynamic-reconfigure

further

Source code for "Influencing Long-Term Behavior in Multiagent Reinforcement Learning" (NeurIPS 2022)

gumbel-rl-gridworld

The use of Gumbel-softmax for a single agent reinforcement learning in a simple gridworld

gym-wolfpack

Implementation of wolfpack domain as in Leibo et al., AAMAS-17

jetson-trashformers

Autonomous humanoid that picks up and throws away trash

mape-tutorial

Tutorial for multi-agent particle environment

meta-mapg

Source code for "A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning" (ICML 2021)

opponent-modeling

dkkim93/meta-mapg

Source code for "A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning" (ICML 2021)

dkkim93/further

Source code for "Influencing Long-Term Behavior in Multiagent Reinforcement Learning" (NeurIPS 2022)

dkkim93/mape-tutorial

Tutorial for multi-agent particle environment

dkkim93/dkkim93.github.io.old

dkkim93/gumbel-rl-gridworld

The use of Gumbel-softmax for a single agent reinforcement learning in a simple gridworld

dkkim93/gym-wolfpack

Implementation of wolfpack domain as in Leibo et al., AAMAS-17

dkkim93/dkkim93.github.io

Dong-Ki Kim's Academic Webpage

dkkim93/cavia

Code for "Fast Context Adaptation via Meta-Learning"

dkkim93/common

dkkim93/gym

A toolkit for developing and comparing reinforcement learning algorithms.

dkkim93/gym-craftenv-render

code for rendering the craft environment in "Modular Multitask Reinforcement Learning with Policy Sketches" (Andreas, Klein, Levine. ICML 2017)

dkkim93/hyper-centralized-learning

dkkim93/lola

Code release for Learning with Opponent-Learning Awareness and variations.

dkkim93/LOLA_DiCE

Pytorch implementation of LOLA (https://arxiv.org/abs/1709.04326) using DiCE (https://arxiv.org/abs/1802.05098)

dkkim93/mctx

Monte Carlo tree search in JAX

dkkim93/MER

Fork of the GEM project (https://github.com/facebookresearch/GradientEpisodicMemory) including Meta-Experience Replay (MER) methods from the ICLR 2019 paper (https://openreview.net/pdf?id=B1gTShAct7)

dkkim93/mer-sac

dkkim93/Minigrid

Simple and easily configurable grid world environments for reinforcement learning

dkkim93/multiagent-competition

Code for the paper "Emergent Complexity via Multi-agent Competition"

dkkim93/multiagent-particle-envs

dkkim93/PettingZoo

Gym for multi-agent reinforcement learning

dkkim93/PPO-PyTorch

Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

dkkim93/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).

dkkim93/pytorch-maml-rl

Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

dkkim93/safety-starter-agents

Basic constrained RL agents used in experiments for the "Benchmarking Safe Exploration in Deep Reinforcement Learning" paper.

dkkim93/spinningup

An educational resource to help anyone learn deep reinforcement learning.

dkkim93/SuperSuit

Easy-to-use micro-wrappers for Gym and PettingZoo based RL Environments

dkkim93/trl

Train transformer language models with reinforcement learning.

dkkim93/ubuntu-misc

Personal ubuntu misc files (e.g., zshrc, vimrc, flake8, terminator)

dkkim93/website