# Tag Info

Accepted

### What is the relation between Q-learning and policy gradients methods?

However, both approaches appear identical to me i.e. predicting the maximum reward for an action (Q-learning) is equivalent to predicting the probability of taking the action directly (PG). Both ...
• 25k
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### How should I handle invalid actions (when using REINFORCE)?

Just ignore the invalid moves. For exploration, it is likely that you won't just execute the move with the highest probability, but instead choose moves randomly based on the outputted probability. If ...
• 4,107

### How should I handle invalid actions (when using REINFORCE)?

Usually softmax methods in policy gradient methods using linear function approximation use the following formula to calculate the probability of choosing action $a$. Here, weights are $\theta$, and ...
• 3,667

### What is the relation between Q-learning and policy gradients methods?

This Tutorial by OpenAI offers a great comparison of different RL methods. I'll try to summarize the differences between Q-Learning and Policy Gradient methods: Objective Function In Q-Learning we ...
• 441
Accepted

• 9,604
Accepted

### How can policy gradients be applied in the case of multiple continuous actions?

As you has said, actions chosen by Actor-Critic typically come from a normal distribution and it is the agent's job to find the appropriate mean and standard deviation based on the the current state. ...
• 3,667
Accepted

### Why does is make sense to normalize rewards per episode in reinforcement learning?

The "trick" of subtracting a (state-dependent) baseline from the $Q(s, a)$ term in policy gradients to reduce variants (which is what is described in your "baseline reduction" link) is a different ...
• 9,604