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Solving CartPole-v1 environment in Keras with Advantage Actor Critic (A2C) algorithm an Deep Reinforcement Learning algorithm

Python 100.00%
reinforcement-learning reinforcement-learning-algorithms advantage-actor-critic cartpole-v1 cartpole-gamebot machine-learning actor-critic

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cartpole-v1-advantage-actor-critic-a2c-keras's Issues

cur_state inaccurate until MINIMUM_REPLAY_MEMORY is reached

        if len(replay_memory) < MINIMUM_REPLAY_MEMORY:
            continue
        # Trinig actor and critic
        train_advantage_actor_critic(replay_memory, actor, critic)
        cur_state = next_state

Note that before the replay_memory reaches the threshold, cur_state is never updated, so the replay memory is flawed.

train_adv_actor_critic logic

Hi,

In the def train_adv_actor_critic function train.py file, when you randomly sample and train the actor with these samples (generated by different policies) isn't that essentially off policy? Whereas A2C is on policy algorithm.
Can you please clarify this and correct me if my understanding is wrong.

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