A Study on the Optimal Design of Reinforced Learning-Driven Personalized Physical Training Strategies in Physical Education Instruction

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

This algorithm and module combination Ave_RMSE enhancement

Lifting amplitude/%		KNNBasic	KNNWithMeans	KNNBaseline	SVD
mL-1m	This algorithm	56.40	56.68	55.18	54.01
	DDPG+RLSTM	51.63	51.94	50.28	49.03
	DDPG+LSTM	34.60	35.02	33.11	31.09
	DDPG+T_self_attention	37.53	37.93	35.79	34.17
	DDPG+self_attention	35.47	35.88	33.67	32.00
mL-100k	This algorithm	75.08	74.45	73.67	74.09
	DDPG+RLSTM	45.95	44.58	42.90	43.82
	DDPG+LSTM	42.15	40.69	38.89	39.87
	DDPG+T_self_attention	53.74	52.58	51.14	51.92
	DDPG+self_attention	45.13	43.74	42.04	42.96

Number A student and physical training strategy set

Number	Degree of recommendation	Sort
1	0.668	3
2	0.526	5
3	0.459	6
4	0.745	2
5	0.324	8
6	0.159	10
7	0.569	4
8	0.951	1
9	0.437	7
10	0.266	9

Female group k-means Cluster results

	Cluster1	Cluster2	Cluster3	Cluster4	Cluster5
Case number	650	710	4520	895	210
Lung capacity score	85	84	85	78	83
50 meters running score	62	68	73	64	37
Fixed jump	64	65	73	35	27
Preflexion score	76	82	84	75	75
Sit-ups scores	65	18	71	68	28
1000 meters run /800 meter running score	34	67	75	65	24
Health score	65.25	69.26	79.83	68.56	52.13

Comparison of experimental results

Algorithm	mL-1m data set		mL-100k data set
	Ave_RMSE	Ave_MAE	Ave_RMSE	Ave_MAE
This algorithm	0.402	0.231	0.243	0.106
DDPG+RLSTM	0.446	0.258	0.527	0.407
DDPG+LSTM	0.603	0.467	0.564	0.436
DDPG+T_self_attention	0.576	0.398	0.451	0.267
DDPG+self_attention	0.595	0.425	0.535	0.368
KNNBasic	0.922	0.726	0.975	0.774
KNNWithMeans	0.928	0.738	0.951	0.75
KNNBaseline	0.897	0.703	0.923	0.727
SVD	0.875	0.683	0.938	0.74

Man group k-means Cluster results

	Cluster1	Cluster2	Cluster3
Case number	1630	2468	1420
Lung capacity score	84	84	83
50 meters running score	81	78	68
Fixed jump	64	65	16
Preflexion score	75	72	63
Sit-ups scores	72	7	5
1000 meters run /800 meter running score	72	63	49
Health score	78.46	68.21	57.44

This algorithm and module combination Ave_MAE enhancement

Lifting amplitude/%		KNNBasic	KNNWithMeans	KNNBaseline	SVD
mL-1m	This algorithm	68.18	68.70	67.14	66.19
	DDPG+RLSTM	64.46	65.04	63.30	62.24
	DDPG+LSTM	35.67	36.72	33.57	31.63
	DDPG+T_self_attention	45.18	46.07	43.39	41.73
	DDPG+self_attention	41.46	42.41	39.54	37.77
mL-100k	This algorithm	86.30	85.90	85.42	85.68
	DDPG+RLSTM	47.42	45.73	44.02	45.00
	DDPG+LSTM	43.67	41.87	40.03	41.08
	DDPG+T_self_attention	66.50	64.40	63.27	63.92
	DDPG+self_attention	52.45	50.93	49.38	50.27