ReinforcementLearning | Simple reinforcement learning algorithms | Reinforcement Learning library

I have been learning Reinforcement Learning for few days now, and I have seen example problems like Mountain Car problem and Cart Pole problem.

In these problems, the way action space is described is discrete. For example in Cart Pole Problem, the agent can either move left or move right.

1. But the examples don't talk about how much? How does the agent decide how much to move left, how much to move right, after all these movements are continuous space actions. So I want to know how does the agent decide what real value to choose from a continuous action space.

2. Also I have been using ReinforcementLearning.jl in Julia and wanted to know a way i could represent range constraints on action space in it. Example, the real value that the agent chooses as it's action should lie in a range like [10.00, 20.00[ for example. I want to know how this can be done.

I am following this tutorial on Policy Gradient using Keras, and can't quite figure out the below.

In the below case, how exactly are input tensors with different shapes fed to the model?
Layers are neither .concated or .Added.

• input1.shape = (4, 4)
• input2.shape = (4,)
• "input" layer has 4 neurons, and accepts input1 + input2 as 4d vector??

The code excerpt (modified to make it simpler) :

I am working with Cython to speed up some python code and I am running into the following error:

I am following this online tutorial for coding a DQN,https://github.com/philtabor/Youtube-Code-Repository/blob/master/ReinforcementLearning/DeepQLearning/torch_deep_q_model.py , however I am running into this Runtime Error that I am unsure of how to debug or modify to prevent this error. Thanks!

I am trying to write my own DQN algorithm in Python, using Tensorflow following the paper(Mnih et al., 2015). In train_DQN function, I have defined the training procedure, and DQN_CartPole is for defining the function approximation(simple 3-layered Neural Network). For loss function, Huber loss or MSE is implemented followed by the gradient clipping(between -1 and 1). Then, I have implemented soft-update method instead of hard-update of the target network by copying the weights in the main network.

I am trying it on the CartPole environment(OpenAI gym), but the rewards does not improve as it does in other people's algorithms, such as keras-rl. Any help will be appreciated.

reward over timestep

If possible, could you have a look at the source code?

• DQN model: https://github.com/Rowing0914/TF_RL/blob/master/agents/DQN_model.py
• Training Script: https://github.com/Rowing0914/TF_RL/blob/master/agents/DQN_train.py
• Reddit post: https://www.reddit.com/r/reinforcementlearning/comments/ba7o55/question_dqn_algorithm_does_not_work_well_on/?utm_source=share&utm_medium=web2x