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View Code? Open in Web Editor NEWSolving CartPole-v1 environment in Keras with Advantage Actor Critic (A2C) algorithm an Deep Reinforcement Learning algorithm
Solving CartPole-v1 environment in Keras with Advantage Actor Critic (A2C) algorithm an Deep Reinforcement Learning algorithm
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.
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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