14,782 research outputs found
Generalized Off-Policy Actor-Critic
We propose a new objective, the counterfactual objective, unifying existing
objectives for off-policy policy gradient algorithms in the continuing
reinforcement learning (RL) setting. Compared to the commonly used excursion
objective, which can be misleading about the performance of the target policy
when deployed, our new objective better predicts such performance. We prove the
Generalized Off-Policy Policy Gradient Theorem to compute the policy gradient
of the counterfactual objective and use an emphatic approach to get an unbiased
sample from this policy gradient, yielding the Generalized Off-Policy
Actor-Critic (Geoff-PAC) algorithm. We demonstrate the merits of Geoff-PAC over
existing algorithms in Mujoco robot simulation tasks, the first empirical
success of emphatic algorithms in prevailing deep RL benchmarks.Comment: NeurIPS 201
Deep Residual Reinforcement Learning
We revisit residual algorithms in both model-free and model-based
reinforcement learning settings. We propose the bidirectional target network
technique to stabilize residual algorithms, yielding a residual version of DDPG
that significantly outperforms vanilla DDPG in the DeepMind Control Suite
benchmark. Moreover, we find the residual algorithm an effective approach to
the distribution mismatch problem in model-based planning. Compared with the
existing TD() method, our residual-based method makes weaker assumptions
about the model and yields a greater performance boost.Comment: AAMAS 202
Beat that Word : How Listeners Integrate Beat Gesture and Focus in Multimodal Speech Discourse
Peer reviewedPublisher PD
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