Comments (2)
dementrock
commented on July 25, 2024
Hi @hashbangCoder, in the DDPG implementation, we compute the on-policy Q value which provides gradient signal to improve the policy, but the sampling of trajectories is done off-policy. The DDPG class accepts a parameter called es, which is abbreviation for exploration strategy. It is then used to generate an off-policy action in this line: https://github.com/rllab/rllab/blob/master/rllab/algos/ddpg.py#L224
Currently we implement two exploration strategies, one is the Brownian motion noise as mentioned in the paper and the other is Gaussian noise. You can find them here: https://github.com/rllab/rllab/tree/master/rllab/exploration_strategies
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hashbangCoder
commented on July 25, 2024
Sheesh can't believe I missed that. My bad. Sorry.
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