Can I add expert data to the replay buffer used by the DDPG algorithm in order to make it converge faster?
Appropriate algorithm for RL problem with sparse rewards, continuous actions and significant stochasticity
Why does this Keras implementation of the DDPG algorithm update the critic's network using the gradient but the pseudocode doesn't?
Why is the behaviour policy denoted by $\beta$ and the exploration policy by $ \mu'$ in the DDPG paper?
Are there examples of agents that use a more modest number of parameters on Pendulum (or similar environments)?
In Deep Deterministic Policy Gradient, are all weights of the policy network updated with the same or different value?
How to avoid rapid actuator movements in favor of smooth movements in a continuous space and action space problem?
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