Papers Multi-Goal Reinforcement Learning
“Multi-Goal Reinforcement Learning” 태그가 달린 논문 34편 · 필터 해제
Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks
Hindsight Experience Replay (HER) is widely regarded as the state-of-the-art algorithm for achieving sample-efficient multi-goal reinforcement learning (RL) in robotic manipulation tasks with binary rewards. HER facilita…
Multi-Goal Reinforcement LearningReinforcement Learning (RL)Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning
Humanoid robots must master numerous tasks with sparse rewards, posing a challenge for reinforcement learning (RL). We propose a method combining RL and automated planning to address this. Our approach uses short goal-co…
Multi-Goal Reinforcement LearningReinforcement Learning (RL)Solving Multi-Goal Robotic Tasks with Decision Transformer
Artificial intelligence plays a crucial role in robotics, with reinforcement learning (RL) emerging as one of the most promising approaches for robot control. However, several key challenges hinder its broader applicatio…
Multi-Goal Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)FDQN: A Flexible Deep Q-Network Framework for Game Automation
In reinforcement learning, it is often difficult to automate high-dimensional, rapid decision-making in dynamic environments, especially when domains require real-time online interaction and adaptive strategies such as w…
Atari GamesDecision MakingMulti-Goal Reinforcement LearningMRHER: Model-based Relay Hindsight Experience Replay for Sequential Object Manipulation Tasks with Sparse Rewards
Sparse rewards pose a significant challenge to achieving high sample efficiency in goal-conditioned reinforcement learning (RL). Specifically, in sequential manipulation tasks, the agent receives failure rewards until it…
FetchPush-v1Multi-Goal Reinforcement LearningReinforcement Learning (RL)Robot ManipulationUnderstanding Hindsight Goal Relabeling from a Divergence Minimization Perspective
Hindsight goal relabeling has become a foundational technique in multi-goal reinforcement learning (RL). The essential idea is that any trajectory can be seen as a sub-optimal demonstration for reaching its final state. …
Imitation LearningMulti-Goal Reinforcement LearningQ-LearningReinforcement Learning (RL)Cluster-based Sampling in Hindsight Experience Replay for Robotic Tasks (Student Abstract)
In multi-goal reinforcement learning with a sparse binary reward, training agents is particularly challenging, due to a lack of successful experiences. To solve this problem, hindsight experience replay (HER) generates s…
ClusteringMulti-Goal Reinforcement LearningOpenAI GymStein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning
In multi-goal Reinforcement Learning, an agent can share experience between related training tasks, resulting in better generalization for new tasks at test time. However, when the goal space has discontinuities and the …
Multi-Goal Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Bilinear value networks
The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function. When learning to achieve multiple goals, data efficiency is intimately connected with the gen…
Multi-Goal Reinforcement LearningGrounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics
This paper focuses on robotic reinforcement learning with sparse rewards for natural language goal representations. An open problem is the sample-inefficiency that stems from the compositionality of natural language, and…
Multi-Goal Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Bi-linear Value Networks for Multi-goal Reinforcement Learning
Universal value functions are used to score the long-term utility of actions to achieve a goal from the current state. In contrast to prior methods that learn a monolithic function to approximate the value, we propose a …
Multi-Goal Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)MHER: Model-based Hindsight Experience Replay
Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally challenging. Existing approaches have utilized goal relabeling on collected experiences to alleviate issues raised from sparse rewa…
modelMulti-Goal Reinforcement Learningreinforcement-learningReinforcement Learning+1Multi-Goal Reinforcement Learning environments for simulated Franka Emika Panda robot
This technical report presents panda-gym, a set Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym. Five tasks are included: reach, push, slide, pick & place and stack. T…
Multi-Goal Reinforcement LearningOpenAI Gymreinforcement-learningReinforcement Learning (RL)Unbiased Methods for Multi-Goal Reinforcement Learning
In multi-goal reinforcement learning (RL) settings, the reward for each goal is sparse, and located in a small neighborhood of the goal. In large dimension, the probability of reaching a reward vanishes and the agent rec…
Multi-Goal Reinforcement LearningQ-Learningreinforcement-learningReinforcement Learning+1Adversarial Intrinsic Motivation for Reinforcement Learning
Learning with an objective to minimize the mismatch with a reference distribution has been shown to be useful for generative modeling and imitation learning. In this paper, we investigate whether one such objective, the …
Multi-Goal Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet
This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By comparing the performances of the Hindsight E…
MuJoCoMulti-Goal Reinforcement LearningOpenAI GymReinforcement Learning (RL)Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning
Multi-goal reinforcement learning is widely applied in planning and robot manipulation. Two main challenges in multi-goal reinforcement learning are sparse rewards and sample inefficiency. Hindsight Experience Replay (HE…
Multi-Goal Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling and ineffective reward functions. In this…
Multi-Goal Reinforcement LearningObjectreinforcement-learningReinforcement Learning+1Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks? When the desired (test time) goal distribution is too distant to offer a useful learning signal, we argue that the…
Multi-Goal Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Counterfactual Data Augmentation using Locally Factored Dynamics
Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses. Though the subprocesses are not independent, their interactions are often …
counterfactualData AugmentationGeneral Reinforcement LearningMulti-Goal Reinforcement Learning+4