在处理连续动作空间的任务时,DDPG 和 SAC 是更好的选择。 我更喜欢 SAC 完全开源(可以直接应用),能直接应用到真实机器人,而且是 DDPG 的改进版本(效果还是第一梯队)。 在需要快速迭代和处理大规模状态空间的任务中,A3C 和 PPO 可能表现更优。 PPO 有严重的采样效率问题,需要海量数据 + 恐怖算力。 OpenAI 提出
RLToolkit is a flexible and high-efficient reinforcement learning framework. Include implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ... - jianzhnie/RLToolkit
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ... License MIT license 0stars857forksBranchesTagsActivity Star Notifications master 2Branches0Tags Code This branch is201 commits ahead of,4 commits behindsweetice/Deep-reinforcement-learning-with-pytorch...
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ... - iffiX/machin
DDPG Episode reward in Pendulum-v0: PPO Original paper: https://arxiv.org/abs/1707.06347 Openai Baselines blog post: https://blog.openai.com/openai-baselines-ppo/ A2C Advantage Policy Gradient, an paper in 2017 pointed out that the difference in performance between A2C and A3C is not obviou...
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ... - sweetice/Deep-reinforcement-learning-with-pytorch
Char05 DDPG Char07 PPO Char08 ACER Char09 SAC Char10 TD3 More figures LICENSE readme.md requirements.txt readme.md Status: Active (under active development, breaking changes may occur) This repository will implement the classic and state-of-the-art deep reinforcement learning al...
DDPG Episode reward in Pendulum-v0: PPO Original paper: https://arxiv.org/abs/1707.06347 Openai Baselines blog post: https://blog.openai.com/openai-baselines-ppo/ A2C Advantage Policy Gradient, an paper in 2017 pointed out that the difference in performance between A2C and A3C is not obviou...