Update README.md

README.md

@@ -66,15 +66,37 @@ Kyle's proficiency in simulating lifelike ragdoll physics is not just a claim; i
 
 The TensorBoard visualizations offer a window into Kyle's training journey and the strides it has made:
 
-![TensorBoard Results 1](results/images/tensorboard_kyle-b0a_001.png "Training Results 1")
+![TensorBoard Results 1](results/images/tensorboard_kyle-b0a_001.png "Training Results 1a")
 *Figure 1: A graph depicting the training progress over time, illustrating a consistent reduction in loss and a corresponding improvement in accuracy.*
 
-![TensorBoard Results 2](results/images/tensorboard_kyle-b0a_002.png "Training Results 2")
+![TensorBoard Results 2](results/images/tensorboard_kyle-b0a_002.png "Training Results 2a")
 *Figure 2: Validation results that shine a light on the model's ability to generalize, indicating robust performance on unseen data.*
 
-![TensorBoard Results 3](results/images/tensorboard_kyle-b0a_003.png "Training Results 3")
+![TensorBoard Results 3](results/images/tensorboard_kyle-b0a_003.png "Training Results 3a")
 *Figure 3: A comparative analysis that lays bare the model's predictions against the ground truth, underscoring the precision of Kyle's learning process.*
 
+We recently have switched over to SAC training algorithms which has yeilded much better performance. The tensorboard charts below reflect the training of Kyle using Sac on a training sequence of 20 million steps. We also set our training buffer to 1 million steps from which our SAC agent learns from.  
+
+![TensorBoard Results 4](results/images/tensorboard_kylebeta4-b0a_001.png "Training Results 1b")
+*Figure 4: A graph depicting the training progress over time, illustrating a consistent reduction in loss and a corresponding improvement in accuracy.*
+
+![TensorBoard Results 5](results/images/tensorboard_kylebeta4-b0a_002.png "Training Results 2b")
+*Figure 5: Validation results that shine a light on the model's ability to generalize, indicating robust performance on unseen data.*
+
+![TensorBoard Results 6](results/images/tensorboard_kylebeta4-b0a_003.png "Training Results 3b")
+*Figure 6: A comparative analysis that lays bare the model's predictions against the ground truth, underscoring the precision of Kyle's learning process.*
+
+And the following is a compariative analysis of a sequence of 20 million turns of PPO verses SAC training. Both of these were trained using the same hardware and software configurations.
+
+![TensorBoard Results 7](results/images/tensorboard_kyle_vs_kylebeta4-b0a_001.png "Training Results 1ab")
+*Figure 7: A graph depicting the training progress over time, illustrating a consistent reduction in loss and a corresponding improvement in accuracy.*
+![TensorBoard Results 8](results/images/tensorboard_kyle_vs_kylebeta4-b0a_002.png "Training Results 2ab")
+*Figure 8: A graph depicting the training progress over time, illustrating a consistent reduction in loss and a corresponding improvement in accuracy.*
+![TensorBoard Results 9](results/images/tensorboard_kyle_vs_kylebeta4-b0a_003.png "Training Results 3ab")
+*Figure 8: A graph depicting the training progress over time, illustrating a consistent reduction in loss and a corresponding improvement in accuracy.*
+![TensorBoard Results 10](results/images/tensorboard_kyle_vs_kylebeta4-b0a_004.png "Training Results 4ab")
+*Figure 8: A graph depicting the training progress over time, illustrating a consistent reduction in loss and a corresponding improvement in accuracy.*
+
 ### The Kyle Model: A Base for Customization
 
 At its core, Kyle is a base biped model designed for direct customization to fit the unique requirements of your project. Whether you aim to fine-tune Kyle for a fighting simulation, choreograph a dance of acrobatics, or develop a system for sorting objects, the model's adaptable nature makes it an ideal starting point. The potential applications are as varied as they are exciting, and we eagerly anticipate the innovative ways in which Kyle will be utilized.