如下图所示,在实现offline goal-conditioned RL 方法的困难在于无法有效地进行长远目标价值函数的学习。当前动作执行错误后的代价较小,不足以引起未来导致策略学习失败的注意。 针对这一问题,作者启发于 大的目标是由多个子目标所构成的思想,进行层次化学习架构的搭建。如下图所示是层次化策略提取的思路,学习子目标,再...
在RL的研究者这里,便将其formulate为一类goal-conditioned强化学习问题。这篇调研也主要调研的是RL学派的相关研究,和机器人的任务关联较少。 问题定义 GCRL的问题相对于一般的RL问题,是在决策基于状态的同时,要求智能体完成某个任务(goal)。通常goal会被augment到state上一起用于任务决策。这样的复杂强化学习问题的决策...
【具身智能】遥操作human2humanoid复现 无特权信息无蒸馏 埃隆猫斯克 498 0 【动作生成】CLoSD Diffusion for multi-task character control 埃隆猫斯克 1164 0 【视觉动捕】2024 SIGGRAPH GVHMR Human Motion Recovery via Gravity-View Coordinates 埃隆猫斯克 4475 0 【动作生成】DART A Diffusion-Based Real-...
Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter is particularly problematic when applying RL to robotics, where detecting whether the desired configuration is reached might ...
Update (Dec 1, 2024):We released a much cleaner implementation of HIQL in theOGBenchrepository, which contains reference implementations of offline goal-conditioned RL algorithms, including GCBC, GCIVL, GCIQL, QRL, CRL, and HIQL. OGBench also provides a number of diverse benchmark environments...
Goal-conditioned RL formalizes the problem of learning different goals in one environment [9]. Similar to conventional RL, the main difference in goal-conditioned RL is that, in this case, the reward function depends on the agent’s goals, which are represented by the states of the system. ...
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2.6 Goal-conditioned RL Goal-conditioned reinforcement learning [44] constructs a goal-conditioned policy to push the agent to acquire new skills and explore novel states. Universal value function approximators [68] sample a fixed goal at the beginning of each episode and reward the agent when the...
This improves the sample efficiency of the goal-conditioned RL. Object Curriculum Learning Since it's difficult to train the grasping policy cross-category due to the topological and geometric variations in different categories, we propose to build an obj...
The goal-conditioned RL demonstration data usually contains multiple excellent demonstration trajectories which are supposed to be highly overlapping. Hence, outliers that deviate from the main subject of the data warrant special concerns. To assess the noisy extent of the trajectory demonstration data, ...