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When implementing DDPG-style policy extraction, we often use a tanh normalizer to bound the action space. That way the policy does not attempt to output something OOD when the Q-function is only trained on actions in [-1, 1]. dist = agent.actor(batch['observations']) normalized_actions = jnp.tanh(dist.loc) q
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