an actor-critic architecture with separate policy and value function networks policy iteration, which alternates between policy evaluation—computing the value function for a policy—and policy improvement—using the value function to obtain a better policy impractical to run either of these steps to co...
PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet.. - quantumiracle/Popular-RL-Algorithms
A state-of-the-art framework, i.e., deep deterministic policy gradient (DDPG), has obtained a certain effect in the robotic control field. When the wheeled mobile robot (WMR) executes operation in unstructured environment, it is critical to endow the WMR with the capacity to avoid the stati...
Section 3 states our problem, and Section 4 introduces and formally describes A3C3, as well as its architecture, modules, limitations, and methodology. Section 5 shows the results of our proposal obtained in complex environments, on two multi-agent environment suites, used by other state-of-...
The proposed architecture was applied successfully in two simulated environments, and a comparison between the two referred techniques was made using the results obtained as a basis and it was demonstrated that the SAC algorithm has a superior performance for the navigation of mobile robots than the...
Trust region policy optimization (TRPO) [23], proximal policy optimization (PPO) [24] and importance weighted actor–learner architecture (IMPALA) [25] change the distribution of the interaction data generated by old or other policies through the importance sampling or V-trace technique, so that ...
environment in the form of tables, which cannot be fully modeled the complex relationship between the environment and action (Mousavi et al., 2016), and a few researchers introduced the deep neural networks into the basic RL model architecture, which is called deep reinforcement learning (DRL)....
Figure 3. Actor-dueling-critic (ADC) networks architecture. It is based on actor-critic architecture. The actor network selects actions based on the policy-gradient method; The dueling-critic network applies dueling architecture to estimate state-action values. The ADC network has better Q-value ...
Figure 3. Architecture of the proposed SAC-based path planning algorithm. Figure 4. Flow chart of the proposed SAC-based path planning algorithm. Figure 5. The workspace of robots. Figure 6. Success ratio by the proposed SAC-based path planning for two open manipulators. Figure 7. Reward fro...
The architecture of the intelligent SMASV navigation system is illustrated in Figure 1. Figure 1. Intelligent navigation system for the SMASV: part (a) is the path planner; part (b) is the sensor module; part (c) is the decision-making module; and part (d) is the control module. ...