<p>I see some issues in your code of the environment. Firstly, and probably most importantly, you should not be incrementing the reward. In your code, every time the agent hits <span class="math-container">$t=475$</span> for example, the reward given by the environment increases by 1. So if the agent oscillates between <span class="math-container">$t=450$</span> and <span class="math-container">$t=475$</span>, at each timestep the environment gives a greater and greater reward. Your Q values have no reason to converge. Instead of incrementing the reward, you should just return a reward of <span class="math-container">$1$</span> at those two desired states and return <span class="math-container">$0$</span> otherwise.</p>
<p>Secondly, it appears that you don't reset the state during <code>reset()</code>, I think you should have <code>self.time = 0</code> in there (unless this was intentional).</p>
<p><strong>Edit</strong> : I also question why you're using DQN for this problem. This problem should be very easy to solve with standard (tabular) Q Learning.</p>
<p><strong>Edit 2</strong> : Your training loop looks wrong. You reset the environment at each iteration in your loop, so your episodes are one timestep long each. What I think you want instead is to have another loop under <code>reset()</code> something like</p>
<pre><code>for episode in range(episodes):
  s = env.reset()
  episode_score = 0 
  for step in range(max_timesteps):
    a = agent.predict_action(s)
    ns, r, d, _ = env.step(a)
    episode_score += r
   ... 
    agent.update_policy()
   ... 
  scores.append(episode_score)
 ... 
</code></pre>

CC BY-SA 4.0
2020-10-11T15:31:18.333Z
thanks, I just corrected the typo in the reset function and changed the reward scheme.. unfortunately, still no convergence towards t = 475 or 450 ... and to answer your question: i am still new to reinforcement learning, and wanted to learn how to implement DQN  on a simple problem, just for the sake of learning and understanding
relaxon
CC BY-SA 4.0
2020-10-11T17:44:55.650Z
I also suggest you increase gamma. Gamma=0.5 is fairly low, and it's very possible that the rewards are just not propagating back to other states due to the exponential decay. Try with gamma=0.99 or something. Not sure if this will be the definitive solution, but it can't hurt.
harwiltz
CC BY-SA 4.0
2020-10-11T17:46:30.987Z
Also, can you describe the results you get? As in, what do you mean by "no convergence towards t=..."? Can you talk about what you expect vs what you observe?
harwiltz
CC BY-SA 4.0
2020-10-11T19:35:34.107Z
thank you, so let me explain: in the end i want the agent to do the following 475 +0 = 475 --> 475 + 0 = 475 --> 475 +0 = 475.... OR something like 475 -25 = 450 --> 450 +25 = 475 --> 475 -25 = 450 .... (i want the sum to always be 475 or 450)... what I observe: it just takes the same action for any state ( for example 450 +25 = 475 --> 475 + 25 = 500 -->  525 + 25 = 550 --> 550 + 25 = 575....).. so it just learns to take always the same action over and over again without caring about the result
relaxon
CC BY-SA 4.0
2020-10-11T21:05:15.607Z
Noticed another issue, updated my original answer.
harwiltz
CC BY-SA 4.0
2020-10-12T08:18:14.947Z
Thank you very much for your comments and suggestions.. unfortunately i still don't get what I expect the agent to learn ( i changed the hyperparameters too, but no success so far)... probably DQN is not very well suited for this type of task as you mentioned
relaxon
CC BY-SA 4.0
2020-10-12T12:54:00.593Z
If you're just doing this as a learning exercise, why don't you try an openai gym environment? DQN definitely can solve cartpole, so I suggest you try that to figure out if there's something wrong with your DQN implementation.
harwiltz
CC BY-SA 4.0
2020-10-11T15:16:25.443Z
2020-10-11T21:04:59.300Z
2020-10-11T21:04:59.300Z
harwiltz
<p>I am pretty new to RL and I am trying to code a simple RL task with pytorch.</p>
<p>The goal/task is the following:
The initial state is <span class="math-container">$t_o$</span> and the agent takes an action <span class="math-container">$\Delta_t$</span>: <span class="math-container">$t_o +\Delta_t = t_1$</span>.</p>
<p>If <span class="math-container">$t_1$</span> equals 450 or 475 then it gets a reward, else he does not get a reward.</p>
<p>I am training the agent with DQN algorithm on a NN ( with: 2 Linear layes: fist layer n_in=1 n_out 128 and second layer n_in=128 and n_out=5):</p>
<blockquote>
<p>observation space(<span class="math-container">$t_i$</span>) is 700 --&gt; <span class="math-container">$t_i \in [0,700[$</span><br />
action space (<span class="math-container">$\Delta_t$</span>) is 5 --&gt; (<span class="math-container">$\Delta_t \in [-50,-25,0,25,50]$</span>)</p>
</blockquote>
<blockquote>
<p>epsilon_start=0.9#e-greedy threshold start value<br />
epsilon_end=0.01#e-greedy threshold end value<br />
epsilon_decay=200#e-greedy threshold decay learning_rate=0.001# NN<br />
optimizer learning rate batch_size=64#Q-learning batch size</p>
</blockquote>
<p>Unfortunately it does not seem to converge to the values <span class="math-container">$t_i=$</span> 450 or 475. I doesn't seem to care about getting a reward.
How can I improve my code so that the agent learns what I am trying to teach him?
I put my code below in case the explanations were not clear enough:</p>
<pre><code>
import gym
from gym import spaces

class RL_env(gym.Env):
    metadata = {'render.modes': ['human']}

    
    def __init__(self):
        super(RL_env, self).__init__()
        
        n_actions_delta = 1 #delta_t
        self.action_space = spaces.Discrete(5)
        
        n_observations = 1 #time
    
        self.observation_space = spaces.Discrete(700)
       
        #initial time
        self.time = 0
        
        self.done = 0
        self.reward = 0

    def reset(self):
        self.reward = 0
        self.done = False
        return self.reward
       
    def step(self,delta_t):
        print('self time',self.time)
        d_t = np.arange(-50,70,25)
        
        self.time = (self.time + d_t[delta_t])%700
        print('delta time',d_t[delta_t],'--&gt;','self time',self.time)
        
       
        
        if self.time == 475 or self.time == 450:
            self.reward = 1
            
            
        else:
            self.reward += 0
        
            
        info = {}
        print('total reward',self.reward)
        print('\n')
        return self.time,self.reward, self.done, info
    

    
    
    def render(self, mode='human', close=False):
        print()
</code></pre>
<pre><code>import gym
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.distributions import Categorical
dtype = torch.float
device = torch.device(&quot;cpu&quot;)
import random
import math
import sys
if not sys.warnoptions:#igrnore warnings
    import warnings
    warnings.simplefilter(&quot;ignore&quot;)

#hyper parameters
epsilon_start=0.9
#e-greedy threshold start value
epsilon_end=0.01#e-greedy threshold end value
epsilon_decay=200#e-greedy threshold decay
learning_rate=0.001# NN optimizer learning rate
batch_size=64#Q-learning batch size 

env = RL_env()


#use replay memory (-&gt; to stabilize and improve our algorithm)for training: store transitions observed by agent,
#then reuse this data later
#sample from it randomly (batch built by transitions are decorrelated)
class ReplayMemory:#allowes the agent to learn from earlier memories (speed up learning and break undesirable temporal correlations)
    def __init__(self, capacity):
        self.capacity = capacity
        self.memory = []
    def push(self, transition):#saves transition
        self.memory.append(transition)
        if len(self.memory)&gt;self.capacity:#if length of memory arra is larger than capacity (fixed)
            del self.memory[0]#remove 0th element

    def sample(self, batch_number):#samples randomly a transition to build batch
        return random.sample(self.memory, batch_number)

    def __len__(self):
        return len(self.memory)
    
#Dqn NN (we want to maximize the discounted, cumulative reward)
#idea of Q-learning: we want to approximate with NN maximal Q-function (gives max return of action in given state)
#training update rule: use the fact that every Q-function for some policy obeys the Bellman equation
#difference between the two sides of the equality is known as the temporal difference error (we want to min -&gt; Huber loss)
#calculate over batch of transitions sampled from the replay memory
class DqnNet(nn.Module):
    def __init__(self):
        super(DqnNet, self).__init__()
        
        state_space = 1
        action_space = env.action_space.n
        num_hid = 128
        self.fc1 = nn.Linear(state_space, num_hid)
        self.fc2 = nn.Linear(num_hid, action_space)
        self.gamma=0.5 #Q-learning discount factor (ensures that reward sum converges, 
                        #makes actions from far future less important)
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.sigmoid(self.fc2(x))
        return x

#select action accordingly to epsilon greedy policy
#sometimes we use model for choosing action, other times sample uniformly 
#probability of choosing a random action will start at epsilon_start and will decay (epsilon_decay) exponentially
#towards epsilon_end
steps_done=0
def predict_action(state):
    global steps_done
    sample=random.random()#random number
    eps_threshold=epsilon_end+(epsilon_start-epsilon_end)*math.exp(-1.*steps_done/epsilon_decay)
    steps_done += 1
    if sample&gt;eps_threshold:  
        x  = eps_threshold,model(Variable(state,).type(torch.FloatTensor)).data.max(0)[1].view(1, 1)
        return x#chose action from model
    
    else:
        x = eps_threshold,torch.tensor([[random.randrange(env.action_space.n)]])
        return x#choose random action uniformly

#wtih the update_policy function we perform a single step of the optimization
#first sample a batch, concatenate all the tensors into a single one, compute Q-value and max Q-value, 
#and combine them into loss
def update_policy():
    if len(memory)&lt;batch_size:#we want to sample a batch of size 64
        return
    transitions = memory.sample(batch_size)#take random transition batch from experience replay memory
    batch_state, batch_action, batch_next_state, batch_reward = zip(*transitions)#convert batch-array of Transitions
                                                                              #to Transition of batch-arrays   
    #--&gt;zip(*) takes iterables as arguments and return iterator
    
    batch_state = Variable(torch.cat(batch_state))#concatenate given sequence tensors in the given dimension
    batch_state = batch_state.resize(batch_size,1)
    batch_action = Variable(torch.cat(batch_action))
    batch_next_state = Variable(torch.cat(batch_next_state))
    batch_next_state = batch_next_state.reshape(batch_size,1)
    batch_reward = Variable(torch.cat(batch_reward))
    
    #print('model batch state',model(Variable(batch_state[0])))
    current_q_values = model(batch_state).gather(1, batch_action)#current Q-values estimated for all actions,
                                                                 #compute Q, then select the columns of actions taken,
                                                                 #these are the actions which would've been taken
                                                                 #for each batch state according to policy_net
    max_next_q_values = model(batch_next_state).detach().max(1)[0]#predicted Q-values for non-final-next-states
                                                                  #(-&gt; gives max Q)
    expected_q_values = batch_reward + (model.gamma * max_next_q_values)

    #loss is measured from error between current and newly expected Q values (Huber Loss)
    loss = F.smooth_l1_loss(current_q_values, expected_q_values)

    # backpropagation of loss to NN --&gt; optimize model
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    return loss, np.sum(expected_q_values.numpy())
    

def train(episodes):
    scores = []
    Losses = []
    Bellman = []
    Epsilon = []
    Times = []
    Deltas = []
    
    
    
    for episode in range(episodes):  
        state=env.reset()#reset environment
        print('\n')
        print('episode',episode)
           
        epsilon_action = predict_action(torch.FloatTensor([state]))
        
        action = epsilon_action[1] #after each time step predict action

        next_state, reward, done,info = env.step(action.item())#step through environment using chosen action
        
        epsilon = epsilon_action[0]
        Epsilon.append(epsilon)
        print(reward,'reward')
              
        state=next_state
        Times.append(state)
        scores.append(reward)            

    
        memory.push((torch.FloatTensor([state]),action,torch.FloatTensor([next_state]),
                         torch.FloatTensor([reward])))#action is already a tensor
        up = update_policy()#update_policy()#update policy
            
        if up != None:
            Losses.append(Variable(up[0]))
            print('loss',Variable(up[0]))
            Bellman.append(up[1])

        #calculate score to determine when the environment has been solved
        mean_score=np.mean(scores[-50:])#mean of score of last 50 episodes
        #every 50th episode print score
        if episode%50 == 0:
            print('Episode {}\tScore: {}\tAverage score(last 50 episodes): {:.2f}'.format(episode,scores[-50:],mean_score))

    
    #print('Losses',Losses)
    Losses = torch.stack(Losses).numpy()
    #print('Losses',Losses)
    plt.plot(np.arange(len(Losses)),Losses)
    plt.xlabel('Training iterations')
    plt.ylabel('Loss')
    plt.show()
    
    Bellman = np.array(Bellman)
    #print('Bellman',Bellman,'\n')
    plt.plot(np.arange(len(Bellman)),Bellman)
    plt.xlabel('Training iterations')
    plt.ylabel('Bellman target')
    plt.show()
    
    #print('scores',scores)
    plt.plot(np.arange(len(scores)),scores)
    plt.xlabel('Training iterations')
    plt.ylabel('Reward')
    plt.show()
    
    #print('epsilon',Epsilon)
    plt.plot(np.arange(len(Epsilon)),Epsilon)
    plt.xlabel('Training iterations')
    plt.ylabel('Epsilon')
    plt.show()
    
    print('Times',Times[-25:])
    print('Deltas',Deltas[-25:])
    
    Times = np.array(Times)
    print('Times',Times)
    #plt.figure(figsize=(31,20))
    plt.figure(figsize=(9,7))
    plt.plot(np.arange(len(Times)),(np.array(Times)))
    plt.xlabel('Training iterations')
    plt.ylabel('t')
    plt.show()
     
    Times_1 = np.array(Times[-300:])
    print('t',Times)
    plt.figure(figsize=(9,7))
    plt.plot(np.arange(len(Times_1)),(np.array(Times_1)))
    plt.xlabel('Last 300 Training iterations')
    plt.ylabel('t')
    plt.ylim(0,1000)
    plt.show()
    
model = DqnNet()#policy         
memory = ReplayMemory(20000)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

train(10000)
</code></pre>

<reinforcement-learning><q-learning><dqn>
Reinforcement learning simple problem: agent not learning, wrong action
relaxon
# Reinforcement learning simple problem: agent not learning, wrong action

(Asked by: username_0 on 2020-10-11)

I am pretty new to RL and I am trying to code a simple RL task with pytorch.
The goal/task is the following:
The initial state is $t_o$ and the agent takes an action $\Delta_t$: $t_o +\Delta_t = t_1$.
If $t_1$ equals 450 or 475 then it gets a reward, else he does not get a reward.
I am training the agent with DQN algorithm on a NN ( with: 2 Linear layes: fist layer n_in=1 n_out 128 and second layer n_in=128 and n_out=5):

observation space($t_i$) is 700 --> $t_i \in [0,700[$
action space ($\Delta_t$) is 5 --> ($\Delta_t \in [-50,-25,0,25,50]$)


epsilon_start=0.9#e-greedy threshold start value
epsilon_end=0.01#e-greedy threshold end value
epsilon_decay=200#e-greedy threshold decay learning_rate=0.001# NN
optimizer learning rate batch_size=64#Q-learning batch size

Unfortunately it does not seem to converge to the values $t_i=$ 450 or 475. I doesn't seem to care about getting a reward.
How can I improve my code so that the agent learns what I am trying to teach him?
I put my code below in case the explanations were not clear enough:

import gym
from gym import spaces

class RL_env(gym.Env):
    metadata = {'render.modes': ['human']}

    
    def __init__(self):
        super(RL_env, self).__init__()
        
        n_actions_delta = 1 #delta_t
        self.action_space = spaces.Discrete(5)
        
        n_observations = 1 #time
    
        self.observation_space = spaces.Discrete(700)
       
        #initial time
        self.time = 0
        
        self.done = 0
        self.reward = 0

    def reset(self):
        self.reward = 0
        self.done = False
        return self.reward
       
    def step(self,delta_t):
        print('self time',self.time)
        d_t = np.arange(-50,70,25)
        
        self.time = (self.time + d_t[delta_t])%700
        print('delta time',d_t[delta_t],'-->','self time',self.time)
        
       
        
        if self.time == 475 or self.time == 450:
            self.reward = 1
            
            
        else:
            self.reward += 0
        
            
        info = {}
        print('total reward',self.reward)
        print('\n')
        return self.time,self.reward, self.done, info
    

    
    
    def render(self, mode='human', close=False):
        print()

import gym
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.distributions import Categorical
dtype = torch.float
device = torch.device("cpu")
import random
import math
import sys
if not sys.warnoptions:#igrnore warnings
    import warnings
    warnings.simplefilter("ignore")

#hyper parameters
epsilon_start=0.9
#e-greedy threshold start value
epsilon_end=0.01#e-greedy threshold end value
epsilon_decay=200#e-greedy threshold decay
learning_rate=0.001# NN optimizer learning rate
batch_size=64#Q-learning batch size 

env = RL_env()


#use replay memory (-> to stabilize and improve our algorithm)for training: store transitions observed by agent,
#then reuse this data later
#sample from it randomly (batch built by transitions are decorrelated)
class ReplayMemory:#allowes the agent to learn from earlier memories (speed up learning and break undesirable temporal correlations)
    def __init__(self, capacity):
        self.capacity = capacity
        self.memory = []
    def push(self, transition):#saves transition
        self.memory.append(transition)
        if len(self.memory)>self.capacity:#if length of memory arra is larger than capacity (fixed)
            del self.memory[0]#remove 0th element

    def sample(self, batch_number):#samples randomly a transition to build batch
        return random.sample(self.memory, batch_number)

    def __len__(self):
        return len(self.memory)
    
#Dqn NN (we want to maximize the discounted, cumulative reward)
#idea of Q-learning: we want to approximate with NN maximal Q-function (gives max return of action in given state)
#training update rule: use the fact that every Q-function for some policy obeys the Bellman equation
#difference between the two sides of the equality is known as the temporal difference error (we want to min -> Huber loss)
#calculate over batch of transitions sampled from the replay memory
class DqnNet(nn.Module):
    def __init__(self):
        super(DqnNet, self).__init__()
        
        state_space = 1
        action_space = env.action_space.n
        num_hid = 128
        self.fc1 = nn.Linear(state_space, num_hid)
        self.fc2 = nn.Linear(num_hid, action_space)
        self.gamma=0.5 #Q-learning discount factor (ensures that reward sum converges, 
                        #makes actions from far future less important)
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.sigmoid(self.fc2(x))
        return x

#select action accordingly to epsilon greedy policy
#sometimes we use model for choosing action, other times sample uniformly 
#probability of choosing a random action will start at epsilon_start and will decay (epsilon_decay) exponentially
#towards epsilon_end
steps_done=0
def predict_action(state):
    global steps_done
    sample=random.random()#random number
    eps_threshold=epsilon_end+(epsilon_start-epsilon_end)*math.exp(-1.*steps_done/epsilon_decay)
    steps_done += 1
    if sample>eps_threshold:  
        x  = eps_threshold,model(Variable(state,).type(torch.FloatTensor)).data.max(0)[1].view(1, 1)
        return x#chose action from model
    
    else:
        x = eps_threshold,torch.tensor([[random.randrange(env.action_space.n)]])
        return x#choose random action uniformly

#wtih the update_policy function we perform a single step of the optimization
#first sample a batch, concatenate all the tensors into a single one, compute Q-value and max Q-value, 
#and combine them into loss
def update_policy():
    if len(memory)zip(*) takes iterables as arguments and return iterator
    
    batch_state = Variable(torch.cat(batch_state))#concatenate given sequence tensors in the given dimension
    batch_state = batch_state.resize(batch_size,1)
    batch_action = Variable(torch.cat(batch_action))
    batch_next_state = Variable(torch.cat(batch_next_state))
    batch_next_state = batch_next_state.reshape(batch_size,1)
    batch_reward = Variable(torch.cat(batch_reward))
    
    #print('model batch state',model(Variable(batch_state[0])))
    current_q_values = model(batch_state).gather(1, batch_action)#current Q-values estimated for all actions,
                                                                 #compute Q, then select the columns of actions taken,
                                                                 #these are the actions which would've been taken
                                                                 #for each batch state according to policy_net
    max_next_q_values = model(batch_next_state).detach().max(1)[0]#predicted Q-values for non-final-next-states
                                                                  #(-> gives max Q)
    expected_q_values = batch_reward + (model.gamma * max_next_q_values)

    #loss is measured from error between current and newly expected Q values (Huber Loss)
    loss = F.smooth_l1_loss(current_q_values, expected_q_values)

    # backpropagation of loss to NN --> optimize model
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    return loss, np.sum(expected_q_values.numpy())
    

def train(episodes):
    scores = []
    Losses = []
    Bellman = []
    Epsilon = []
    Times = []
    Deltas = []
    
    
    
    for episode in range(episodes):  
        state=env.reset()#reset environment
        print('\n')
        print('episode',episode)
           
        epsilon_action = predict_action(torch.FloatTensor([state]))
        
        action = epsilon_action[1] #after each time step predict action

        next_state, reward, done,info = env.step(action.item())#step through environment using chosen action
        
        epsilon = epsilon_action[0]
        Epsilon.append(epsilon)
        print(reward,'reward')
              
        state=next_state
        Times.append(state)
        scores.append(reward)            

    
        memory.push((torch.FloatTensor([state]),action,torch.FloatTensor([next_state]),
                         torch.FloatTensor([reward])))#action is already a tensor
        up = update_policy()#update_policy()#update policy
            
        if up != None:
            Losses.append(Variable(up[0]))
            print('loss',Variable(up[0]))
            Bellman.append(up[1])

        #calculate score to determine when the environment has been solved
        mean_score=np.mean(scores[-50:])#mean of score of last 50 episodes
        #every 50th episode print score
        if episode%50 == 0:
            print('Episode {}\tScore: {}\tAverage score(last 50 episodes): {:.2f}'.format(episode,scores[-50:],mean_score))

    
    #print('Losses',Losses)
    Losses = torch.stack(Losses).numpy()
    #print('Losses',Losses)
    plt.plot(np.arange(len(Losses)),Losses)
    plt.xlabel('Training iterations')
    plt.ylabel('Loss')
    plt.show()
    
    Bellman = np.array(Bellman)
    #print('Bellman',Bellman,'\n')
    plt.plot(np.arange(len(Bellman)),Bellman)
    plt.xlabel('Training iterations')
    plt.ylabel('Bellman target')
    plt.show()
    
    #print('scores',scores)
    plt.plot(np.arange(len(scores)),scores)
    plt.xlabel('Training iterations')
    plt.ylabel('Reward')
    plt.show()
    
    #print('epsilon',Epsilon)
    plt.plot(np.arange(len(Epsilon)),Epsilon)
    plt.xlabel('Training iterations')
    plt.ylabel('Epsilon')
    plt.show()
    
    print('Times',Times[-25:])
    print('Deltas',Deltas[-25:])
    
    Times = np.array(Times)
    print('Times',Times)
    #plt.figure(figsize=(31,20))
    plt.figure(figsize=(9,7))
    plt.plot(np.arange(len(Times)),(np.array(Times)))
    plt.xlabel('Training iterations')
    plt.ylabel('t')
    plt.show()
     
    Times_1 = np.array(Times[-300:])
    print('t',Times)
    plt.figure(figsize=(9,7))
    plt.plot(np.arange(len(Times_1)),(np.array(Times_1)))
    plt.xlabel('Last 300 Training iterations')
    plt.ylabel('t')
    plt.ylim(0,1000)
    plt.show()
    
model = DqnNet()#policy         
memory = ReplayMemory(20000)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

train(10000)

**Tags:** reinforcement-learning, q-learning, dqn


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## 1 answers

### Answer by username_1 on 2020-10-11. Score: 3

I see some issues in your code of the environment. Firstly, and probably most importantly, you should not be incrementing the reward. In your code, every time the agent hits $t=475$ for example, the reward given by the environment increases by 1. So if the agent oscillates between $t=450$ and $t=475$, at each timestep the environment gives a greater and greater reward. Your Q values have no reason to converge. Instead of incrementing the reward, you should just return a reward of $1$ at those two desired states and return $0$ otherwise.
Secondly, it appears that you don't reset the state during reset(), I think you should have self.time = 0 in there (unless this was intentional).
Edit : I also question why you're using DQN for this problem. This problem should be very easy to solve with standard (tabular) Q Learning.
Edit 2 : Your training loop looks wrong. You reset the environment at each iteration in your loop, so your episodes are one timestep long each. What I think you want instead is to have another loop under reset() something like
for episode in range(episodes):
  s = env.reset()
  episode_score = 0 
  for step in range(max_timesteps):
    a = agent.predict_action(s)
    ns, r, d, _ = env.step(a)
    episode_score += r
   ... 
    agent.update_policy()
   ... 
  scores.append(episode_score)
 ...

> **Comments:**
> **username_0**: thanks, I just corrected the typo in the reset function and changed the reward scheme.. unfortunately, still no convergence towards t = 475 or 450 ... and to answer your question: i am still new to reinforcement learning, and wanted to learn how to implement DQN  on a simple problem, just for the sake of learning and understanding  
> **username_1**: I also suggest you increase gamma. Gamma=0.5 is fairly low, and it's very possible that the rewards are just not propagating back to other states due to the exponential decay. Try with gamma=0.99 or something. Not sure if this will be the definitive solution, but it can't hurt.  
> **username_1**: Also, can you describe the results you get? As in, what do you mean by "no convergence towards t=..."? Can you talk about what you expect vs what you observe?  
> **username_0**: thank you, so let me explain: in the end i want the agent to do the following 475 +0 = 475 --> 475 + 0 = 475 --> 475 +0 = 475.... OR something like 475 -25 = 450 --> 450 +25 = 475 --> 475 -25 = 450 .... (i want the sum to always be 475 or 450)... what I observe: it just takes the same action for any state ( for example 450 +25 = 475 --> 475 + 25 = 500 -->  525 + 25 = 550 --> 550 + 25 = 575....).. so it just learns to take always the same action over and over again without caring about the result  
> **username_1**: Noticed another issue, updated my original answer.  
> **username_0**: Thank you very much for your comments and suggestions.. unfortunately i still don't get what I expect the agent to learn ( i changed the hyperparameters too, but no success so far)... probably DQN is not very well suited for this type of task as you mentioned  
> **username_1**: If you're just doing this as a learning exercise, why don't you try an openai gym environment? DQN definitely can solve cartpole, so I suggest you try that to figure out if there's something wrong with your DQN implementation.  

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