MuJoCo Games
17개 벤치마크 · 논문 8편 · 이 태스크의 논문 보기 →
Benchmarks
Ant
Walker2d
Ant-v3
HalfCHeetah-v3
HalfCheetah
Hopper
Hopper-v3
Humanoid-v2
Humanoid-v3
InvertedDoublePendulum
InvertedPendulum
Point Maze
Reacher
Sawyer Pusher
Sweeper
Swimmer
Walker2d-v3
Most implemented
IQ-Learn: Inverse soft-Q Learning for Imitation
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning
EDGE: Explaining Deep Reinforcement Learning Policies
Weak Human Preference Supervision For Deep Reinforcement Learning
Papers
Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network
Evolutionary Reinforcement Learning (ERL), training the Reinforcement Learning (RL) policies with Evolutionary Algorithms (EAs), have demonstrated enhanced exploration capabilities and greater robustness than using tradi…
Evolutionary AlgorithmsMuJoCoMuJoCo GamesReinforcement Learning (RL)LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning
Recent methods for imitation learning directly learn a $Q$-function using an implicit reward formulation rather than an explicit reward function. However, these methods generally require implicit reward regularization to…
Continuous ControlImitation LearningMuJoCo GamesQ-Learning+3A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gradient algorithm. Our contribution is demonstrating the virtues of magnetic mirro…
Deep Reinforcement LearningMuJoCo Gamesreinforcement-learningReinforcement Learning+1EDGE: Explaining Deep Reinforcement Learning Policies
With the rapid development of deep reinforcement learning (DRL) techniques, there is an increasing need to understand and interpret DRL policies. While recent research has developed explanation methods to interpret how a…
Deep Reinforcement LearningMuJoCoMuJoCo Gamesreinforcement-learning+3Particle Based Stochastic Policy Optimization
Stochastic polic have been widely applied for their good property in exploration and uncertainty quantification. Modeling policy distribution by joint state-action distribution within the exponential family has enabled …
Deep Reinforcement LearningMuJoCo GamesOffline RLReinforcement Learning (RL)+1IQ-Learn: Inverse soft-Q Learning for Imitation
In many sequential decision-making problems (e.g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task. However, imitation learning (IL) …
Atari GamesContinuous ControlDecision MakingImitation Learning+3