The average cumulative reward of algorithms. حسب Jianbin Zheng (587000)

"…The algorithm employs recurrent neural networks to capture and process historical information. …"

Reward function related parameters. حسب Honglei Pang (22693724)

الموضوعات:

Research efforts in designing reward function for the AD problem with different criteria. حسب Nesma M. Ashraf (10954037)

Comparative validation of TIS indicator in reward functions. حسب Yulin Tian (1457986)

الموضوعات:

Initial values of the reward shaping function components as individual’s chromosome. حسب Larasmoyo Nugroho (18078260)

Framework of the proposed signal control algorithm. حسب Hyosun Lee (1567246)

Reward function weight combinations. حسب Bosen Zeng (22404042)

Comparative validation of overflow state feedback indicator in reward functions. حسب Yulin Tian (1457986)

الموضوعات:

Reward curve of DE-MADDPG algorithm in circular shaft-hole and square shaft-hole assembly. حسب Guohua Cao (697580)

Results of proposed algorithms and algorithms in [9] for 18 instances. حسب Jin Zhang (53297)

Sound version of the SF transfer algorithm’s policy after learning for 25 episodes in scale task 2. حسب Lucas Lehnert (9525615)

Flowchart of step() function. حسب Raed Alharthi (18340157)

Pseudo-code of DMDDPG algorithm. حسب Guohua Cao (697580)

"…First, we analyze the stages of hole-seeking, alignment, and insertion in the shaft-hole assembly process, based on a comprehensive study of the interactions between shafts and holes. Next, a reward function is designed by integrating the decoupled multi-agent deterministic deep deterministic policy gradient (DMDDPG) algorithm. …"

Comparison of assembly results of three algorithms. حسب Guohua Cao (697580)

Flowchart of DQN algorithm. حسب Jiandong Qiu (20389944)

Comparison of different algorithms. حسب Jiandong Qiu (20389944)

Reward outcomes for each combination of task cue and policy in the experiment. حسب Sam Hall-McMaster (10343795)

A more detailed interaction between DDPG controller agent–GA-searched reward shaping function–Environment. حسب Larasmoyo Nugroho (18078260)

Route for bays29 output by ABSQL algorithm. حسب Jin Zhang (53297)

"…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …"

Simulation parameters for the QL, ABSQL and DSRABSQL algorithms. حسب Jin Zhang (53297)