Questions tagged [actor-critic]

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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....
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43 views

RL PPO Algorithm: Understanding the Value Function Loss term in PPO by OpenAI

In the Schulman 2017 PPO Paper, there is a value function loss term in the final loss in equation 9, where they state that the value function loss is the MSE of the target value and predicted value. ...
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75 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 ...
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171 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 ...
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0answers
20 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 ...
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33 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 ...
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0answers
25 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 ...
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1answer
89 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(...
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1answer
45 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, ...
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0answers
234 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 ...
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1answer
53 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 ...
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1answer
307 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 ...
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1answer
3k 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 ...
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2answers
2k 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 ...
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1answer
47 views

Reward is converging but actions taken by trained agent are illogical in reinforcement learning

I am training a reinforcement learning agent using DQN. My state space has 6 variables and the agent can one action which is discretized into 500 actions My reward structure looks like ...
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1answer
37 views

Why can't Policy Gradient Algorithm be seen as an Actor-Critic Method?

During the equation deducing in policy gradient algorithm(e.g., REINFORCE), we are actually using an expectancy of total reward, which we try to maximize. $$\overline{R_\theta}=E_{\tau\sim\pi_\theta}[...
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1answer
673 views

A2C Continuous for Pendulum-v0 working implementation, negation for loss and entropy calculation

very good implementation of A2C continuous for Pendulum-v0 Code has snippet to stop execution when mean of last 10 or 20 is higher than -20 but the results look like: ...
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1answer
403 views

multipying negated gradients by actions for the loss in actor nn of DDPG

In this Udacity project code that I have been combing through line by line to understand the implementation, I have stumbled on a part in class Actor where this ...
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0answers
42 views

Reinforcement learning - generating a matrix of continuous values with varying size for test data generation

Currently, I am using RL A3C algorithm for test data generation, where for a set of 30 functions written in C (mostly basic algorithms like Prime number checks, triangle validity, etc.) I try to ...
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2answers
64 views

Proof subtracting baseline doesn't influence gradient can be used to show no gradient exist at all?

I am using David Silver's course in RL to help me write my thesis. However, I am baffled by the proof given in lecture 7 slide 29: slideshow \begin{align} \mathbb{E}_{\pi_\theta}[\nabla_\theta \log_\...
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1answer
95 views

Stability of value function approximation in policy gradients

In DQNs, function approximation of the Q-values is unstable for correlated updates. In policy gradients with a baseline, will the value function of the policy not be plagued by the same correlated ...
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1answer
309 views

Time horizon T in policy gradients (actor-critic)

I am currently going through the Berkeley lectures on Reinforcement Learning. Specifically, I am at slide 5 of this lecture. At the bottom of that slide, the gradient of the expected sum of rewards ...
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2answers
606 views

A3C - Turning action probabilities into intensities

I'm experimenting with using an A3C network to learn to play old Atari video games. My network outputs a set of probabilities for each possible action (e.g. left, right, shoot), and I use this ...
3
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1answer
988 views

How to design two different neural nets for actor and critic RL?

In order to have an actor critic RL model there are two things to be satisfied . Value approximation function should converge to a local minimum $$\sum_s d^{\pi}(s) \sum_a \pi(s,a)[Q^{\pi}(s,a) - ...