# Questions tagged [actor-critic]

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### Bellman Error for Value Function $V$

I am trying to create a variant of DDPG in MATLAB that has no action-value $\langle Q \rangle$ net, but that instead works with networks $\langle V \rangle, \langle f \rangle, \langle r \rangle$, and ...
1 vote
26 views

### Actor-Critic one step TD update rule

In Sutton & Barto's book (Chapter $13$), it is stated that the update rule in REINFORCE could be reformated as \begin{equation} \begin{split} \theta_{t+1} &=\theta_t+\alpha\left(G_{t:t+1}-\hat{...
16 views

### Why is the optimal output out of domain in A2C?

If each state has an optimal action, then the optimal actions distribution vector is a one-hot vector kind of like [0,0,1,0,0,0]. But with algorithms like A2C, we ...
1 vote
68 views

### What is the meaning about the $\alpha$ in TD3 algorithm

I am study the paper with TD3 algorithm. I am curious about the meaning of $\alpha$ while the paper prove that overestimation will be happened in a critical situation. The contents about mathematical ...
1 vote
248 views

### Soft actor-critic reinforcement learning for 100x100 maze environment

I am doing a project which requires a soft actor-critic reinforcement learning agent to learn how to reach a goal in a 100x100 maze environment as the one below: The state space is discrete and only ...
309 views

### Actor Network Target Value in A2C Reinforcement Learning

In DQN, we use; $Target = r+\gamma v(s')$ equation to train (fit) our network. It is easy to understand since we use the $Target$ value as the dependent variable like we do in supervised learning. I....
1 vote
1k views

### Reinforcement Learning - PPO: Why do so many implementations calculate the returns using the GAE? (Mathematical reason)

There are so many PPO implementations that use GAE and do the following: ...
1 vote
135 views

### A2C learning very slowly when I try to make it learn on batches as compared to making it learn on each step

I tried this on openai gym environment - LunarLander-v2. I wrote two algorithms with just one difference: Made it learn on each step. Made it learn at the end of each episode. There is a ...
1 vote
235 views

### Pytorch XLA to solve the spawn problems in a Colab Env

As reference only, here is my code It seems that torch.multiprocessing.set_start_method("spawn") can't be used in an Colab Env. Only 'fork' is allowed. I have ...
1 vote
42 views

### Action selection in actor-critic algorithm:

I have an action space that is just a list of values given by acts = [i for i in range(10, 100, 10)]. According to pytorch documentary, the loss is calculated as below. Could someone explain to me how ...
49 views

### Actions taken by agentn/ agent performance not improving

Hi I am trying to develop an rl agent using PPO algorithm. My agent takes an action(CFM) to maintain a state variable called RAT in between 24 to 24.5. I am using PPO algorithm of stable-baselines ...
1 vote
80 views

### Rewards are converged but with a lot of variations

I am training a reinforcement learning agent on an episodic task of fixed episode length. I am tracking the training process by plotting the cumulative rewards over an episode. I am using tensorboard ...
463 views

### Having a reward structure which gives high positive rewards compared to the negative rewards

I am training an RL agent using PPO algorithm for a control problem. The objective of the agent is to maintain temperature in a room. It is an episodic task with episode length of 9 hrs and step size(...
1 vote
65 views

### Formulation of a reward structure

I am new to reinforcement learning and experimenting with training of RL agents. I have a doubt about reward formulation, from a given state if a agent takes a good action i give a positive reward, ...
1 vote
291 views

### Actor Critic Model implementation

I am going to work on a project which requires implementation of A2C model using Tensorflow 2.0. I am new in the Machine Learning field and also in Python. These are topics which I have covered ...
166 views

### How to handle differences between training and deploying of an RL agent

Hi I am training an RL agent for a control problem. The objective of the agent is to maintain temperature in a zone. It is an episodic task with episode length of 10 hrs and actions being taken every ...
1 vote
1k views

### Different results every time I train a reinforcement learning agent

I am training an RL agent for a control problem using PPO algorithm. I am using stable-baselines library for it. The objective of an agent is to maintain a temperature of 24 deg in a zone and it ...
5k views

### Evaluating a trained Reinforcement Learning Agent?

I am new to reinforcement learning agent training. I have read about PPO algorithm and used stable baselines library to train an agent using PPO. So my question here is how do I evaluate a trained RL ...
4k views

### Agent always takes a same action in DQN - Reinforcement Learning

I have trained an RL agent using DQN algorithm. After 20000 episodes my rewards are converged. Now when I test this agent, the agent is always taking the same action , irrespective of state. I find ...