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for e in tqdm(range(0, num_of_episodes)):
# Reset the enviroment
state = enviroment.reset()
# Initialize variables
reward = 0
terminated = False
for timestep in range(timesteps_per_episode):
state = img_processor.process_env_state(state)
# Run Action
action = agent.act(state)
# Take action
next_state, reward, terminated, info = enviroment.step(action)
next_state = img_processor.process_env_state(next_state), action, reward, next_state, terminated)
state = next_state
if terminated:
if len(agent.expirience_replay) > batch_size:
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