use gymnasium instead of gym

Reinforcement Learning/CartPole/cartpole-DQN.py

@@ -1,5 +1,5 @@
 import random
-import gym
+import gymnasium as gym
 import numpy as np
 from collections import deque
 from keras.models import Sequential
@@ -19,7 +19,7 @@
 
 EXPLORATION_MAX = 1.0
 EXPLORATION_MIN = 0.01
-EXPLORATION_DECAY = 0.995
+EXPLORATION_DECAY = 0.
 
 
 class DQNSolver:
@@ -59,7 +59,7 @@ def experience_replay(self):
             q_values[0][action] = q_update
             #print(state.shape, q_values.shape)
             x[i], y[i] = state, q_values
-        self.model.fit(x, y, verbose=1)
+        self.model.fit(x, y, verbose=0)
         self.exploration_rate *= EXPLORATION_DECAY
         self.exploration_rate = max(EXPLORATION_MIN, self.exploration_rate)
 
@@ -73,14 +73,15 @@ def cartpole():
     run = 0
     while True:
         run += 1
-        state = env.reset()
+        # print(env.reset())
+        state = env.reset()[0]
         state = np.reshape(state, [1, observation_space])
         step = 0
         while True:
             step += 1
             #env.render()
             action = dqn_solver.act(state)
-            state_next, reward, terminal, info = env.step(action)
+            state_next, reward, terminal, truncated, _ = env.step(action)
             reward = reward if not terminal else -reward
             state_next = np.reshape(state_next, [1, observation_space])
             dqn_solver.remember(state, action, reward, state_next, terminal)