Temporal Difference Learning - SARSA & Q-Learning
LINK@KoreaTech
Febraury, 14, 2019
Email: link.koreatech at gmail.com
This site is made by using the source codes shared from the site, REINFORCEjs
Febraury, 14, 2019
Email: link.koreatech at gmail.com
This site is made by using the source codes shared from the site, REINFORCEjs
SARSA
- Sum of all state values: -1
- Total number of steps over all episodes: -1
- Status: RESET
- Epsilon: 0.2
- Discount Factor (γ): 0.75
- Initial epsilon value for ε-greedy policy (ε): 0.2
- Epsilon decay rate (η): 0.02
- Learning rate (α): 0.1
Sum of each step's reward over episodes:
Episode: 0
Q-learning
- Sum of all state values: -1
- Total number of steps over all episodes: -1
- Status: RESET
- Epsilon: 0.2
- Discount Factor (γ): 0.75
- Initial epsilon value for ε-greedy policy (ε): 0.2
- Epsilon decay rate (η): 0.02
- Learning rate (α): 0.1
Sum of each step's reward over episodes:
Episode: 0
