Temporal-difference (TD) learning is a type of reinforcement learning that updates the value function based on the current state and the expected future reward. This approach is used in a variety of applications, including game playing, robotics, and finance.
TD Learning Algorithm
The basic TD learning algorithm is as follows:
“`
V(s) <- V(s) + alpha * (R(s) + gamma * V(s') - V(s))
```
where:
* V(s) is the estimated value of state s
* R(s) is the reward for being in state s
* gamma is the discount factor (0 <= gamma <= 1)
* V(s') is the estimated value of the next state s'
* alpha is the learning rate (0 < alpha < 1)
The learning rate, alpha, controls how quickly the value function is updated. A higher alpha will result in faster learning, but it can also lead to instability. The discount factor, gamma, controls the trade-off between immediate and future rewards. A higher gamma will result in the value function being more focused on future rewards.
TD Learning Variants
There are a number of different TD learning variants, including:
* **SARSA:** This variant of TD learning uses the same action in both the current and next states. This can be useful for learning in environments where the actions have a delayed effect.
* **Q-learning:** This variant of TD learning learns the value of state-action pairs. This can be useful for learning in environments where the rewards are sparse.
* **Expected SARSA:** This variant of TD learning uses the expected value of the next action in the next state. This can be useful for learning in environments with stochastic transitions.
TD Learning Applications
TD learning is used in a variety of applications, including:
* **Game playing:** TD learning is used in a variety of games, including chess, checkers, and backgammon.
* **Robotics:** TD learning is used to control robots in a variety of tasks, such as navigation and manipulation.
* **Finance:** TD learning is used to model financial markets and to make investment decisions.
Conclusion
TD learning is a powerful reinforcement learning algorithm that can be used to solve a wide variety of problems. This approach is relatively simple to implement and it can be used in a variety of applications.
Kind regards J.O. Schneppat.
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