Double Q-learning
1. Double Q-learning
Another advanced technique we're going to delve into is double Q-learning. This method enhances the traditional Q-learning approach, addressing some of its challenges to improve learning stability and efficiency.2. Q-learning
Q-learning has been a cornerstone in our exploration of RL, teaching agents to navigate environments by estimating the optimal action-value function. However, Q-learning has a tendency to overestimate Q-values due to taking into consideration the maximum Q when updating Q-values. This overestimation can lead to suboptimal policy learning, especially in environments with noisy or stochastic rewards.3. Double Q-learning
Double Q-learning mitigates this overestimation bias by maintaining two separate Q-tables, Q0 and Q1. Each table is updated using information from the other, thus reducing the risk of overestimating Q-values. The key insight behind Double Q-learning is that by splitting the maximization step between two tables, we obtain a more accurate estimate of the action-value function.4. Double Q-learning updates
Here's how Double Q-learning works: To update the Q-value for a chosen action, Double Q-learning randomly selects one of the two Q-tables for updating.5. Q0 update
Let's say it picks Q0. It then uses Q0 to determine the best next action but updates its value based on the reward observed and the estimated value of the next action from Q1.6. Q1 update
If it picks Q1, it uses Q1 to determine the best next action but it updates the Q-value based on the reward observed and the estimated value of the next action from Q0.7. Double Q-learning
This cross-table update method effectively reduces the bias introduced by the maximization step in traditional Q-learning. The process alternates between Q0 and Q1 for action-value updates, ensuring both tables contribute to the learning process.8. Implementation with Frozen Lake
Now, let's put this into practice in the Frozen Lake environment. We start by initializing the environment, along with two Q-tables inside a list Q, having the same size, both filled with zeros. These tables represent our dual estimators for the state-action values. Also, we define the number of training episodes, the learning rate, and the discount factor.9. Implementing update_q_tables()
The update_q_tables() function is where we define the update rule for double q-learning. For each action taken in the environment, we decide randomly to update one of the tables by selecting a random index, i, which will be either 0 or 1. Then, we identify the best_next_action using the chosen Q-table, Q[i], but update Q[i]'s value for the current state-action pair using the Q[1-i]'s estimate for the next state and the chosen action. That way, to update Q[0], we use Q[1]'s estimate, and to update Q[1], we use Q[0]'s estimate.10. Training
The agent undergoes training over the specified number of episodes, with each step within an episode following the Double Q-learning update rules using the update_q_tables() function we defined earlier. Once training is complete, we combine the knowledge of the Q-tables by either averaging them or summing them. In this example, we will go with the sum.11. Agent's policy
Finally, we print the policy derived from the resulting Q-table. As we can see, the agent converged to the optimal policy by learning how to successfully navigate the lake, avoiding falling into the holes.12. Let's practice!
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