PyTorch-ActorCriticRL PyTorch implementation of continuous action actor-critic algorithm. The algorithm uses DeepMind's Deep Deterministic Policy Gradient DDPG method for updating the actor and critic networks along with Ornstein–Uhlenbeck process for exploring in continuous action space while using a Dete...
Furthermore, the actor–critic RL simulation training subsystem includes two modules: (1) Parallel data sampling, utilizing one single GUP and multiple CPUs to speed up the parafoil motion-control-simulation training process; (2) Actor–critic reinforcement learning algorithm (Agent), using the data...
Critic Network: The neural network layer structure of the critic model is as follows: The critic network’s architecture, as shown in Algorithm 2, consists of one input layer, two hidden layers, and one output layer. Like the actor network, the input layer has 12 neurons that accept the ...
4. Advantage Actor-Critic for Autonomous Intersection Management In this section, we introduce the A2C model for AIM, which contains the state space, action space, reward, and learning algorithm. 4.1. State Space In the design of the state space, it is necessary to consider the kind of desig...
4. Advantage Actor-Critic for Autonomous Intersection Management In this section, we introduce the A2C model for AIM, which contains the state space, action space, reward, and learning algorithm. 4.1. State Space In the design of the state space, it is necessary to consider the kind of desig...
No navigation algorithm will be used. The variables will be sent directly to the PID by the UUV simulator, with added noises (described in Section 4.1.3). 4.2.2. Implementation of the Soft Actor–Critic Algorithm for AUV Control The deep reinforcement learning (deep RL) algorithm we chose ...
3. State Super Sampling Soft Actor–Critic (S4AC) Algorithm In this section, the construction of the SSIG objective function is presented, followed by a description of using SSIG to generate predictive states of a dynamic system. Afterwards, the action–value function of a multi-agent version...
Therefore, in the present research, we determine whether DP can be applied to the actor-critic algorithm, a representative policy-based method. In this approach, an actor takes actions and updates its parameters by directly interacting with an environment; however, the critic can estimate the ...