The LPT3 algorithm utilizes LSTM networks to process sequential state information and combines PER and TD3 methods to achieve efficient continuous control. It is capable of learning accurate policies in high-dimensional state spaces, improving learning efficiency and performance through experience replay and policy evaluation.

The implementation of LSTM-TD3 proposed in Memory-based Deep Reinforcement Learning for POMDP.

PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet.. - maywind23/LSTM-RL

2021年2月24日 · In this paper, we propose Long-Short-Term-Memory-based Twin Delayed Deep Deterministic Policy Gradient (LSTM-TD3) by introducing a memory component to TD3, and compare its performance with other DRL algorithms in both MDPs and POMDPs.

In this paper, we propose Long-Short- Term-Memory-based Twin Delayed Deep Deterministic Policy Gradient (LSTM-TD3) by introducing a memory component to TD3, and compare its performance with other DRL algorithms in both MDPs and POMDPs.

Twin Delayed Deep Deterministic policy gradient (TD3)是由Scott Fujimoto等人在Deep Deterministic Policy Gradient (DDPG)算法上改进得到的一种用于解决连续控制问题的在线(on-line)异策(off-policy)式深度强化学习算法。本质上，TD3算法就是将Double Q-Learning算法的思想融入到DDPG算法中。

This repository implementes the LSTM-TD3 proposed in Memory-based Deep Reinforcement Learning for POMDP. The baselines are based on the implementations provided in Spinning Up with two key changes: env_wrapper is added to implement POMDP-version of the tasks in MuJoCo and PyBullet; lstm_td3 is the implementation of the proposed method

2022年3月10日 · Thus, a deep reinforcement learning based task offloading algorithm, named LSTM-TD3, is proposed to solve the formulated problem. Specifically, LSTM-TD3 incorporates the long short-term memory (LSTM) and twin delayed deep deterministic policy gradient algorithm (TD3), and can leverage long-term environment information to efficiently explore the ...

We dubbed this new method as PL-TD3. Firstly, we improve the convergence speed of the algorithm by introducing PER strategy. Secondly, we use LSTM neural network to achieve the improvement of the algorithm for dynamic obstacle perception.

2021年1月3日 · TD3是一种确定性策略强化学习算法，适合于高维连续动作空间。 它的优化目标很简单： 用大白话来讲，就是我要在不同的state下找到对应的action，使得我与环境互动的分数最高。 这很直观，有没有~ 为了做到这一件事情，我们分别需要一个Actor和一个Critic。 Actor将不同的state映射到对应的action上去，大白话来讲就是决定Agent在各个state下具体采取什么样的动作。 而Critic就是为了告诉Agent在不同的state下，采取不同action的最终到底会得多少分，这能帮 …