Transcription of CONTINUOUS CONTROL WITH DEEP REINFORCEMENT …
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CONTINUOUS CONTROL WITH DEEP REINFORCEMENT …
Published as a conference paper at ICLR 2016 CONTINUOUS CONTROL WITH DEEP REINFORCEMENTLEARNINGT imothy P. Lillicrap , Jonathan J. Hunt , Alexander Pritzel, Nicolas Heess,Tom Erez, Yuval Tassa, David Silver & Daan WierstraGoogle DeepmindLondon, UK{countzero, jjhunt, apritzel, heess,etom, tassa, davidsilver, wierstra}@ adapt the ideas underlying the success of Deep Q-Learning to the continuousaction domain. We present an actor-critic, model-free algorithm based on the de-terministic policy gradient that can operate over CONTINUOUS action spaces. Usingthe same learning algorithm, network architecture and hyper-parameters, our al-gorithm robustly solves more than 20 simulated physics tasks, including classicproblems such as cartpole swing-up, dexterous manipulation, legged locomotionand car driving. Our algorithm is able to find policies whose performance is com-petitive with those found by a planning algorithm with full access to the dynamicsof the domain and its derivatives.
on the deterministic policy gradient (DPG) algorithm (Silver et al., 2014) (itself similar to NFQCA (Hafner & Riedmiller, 2011), and similar ideas can be found in (Prokhorov et al., 1997)). However, as we show below, a naive application of this actor-critic method with neural function approximators is unstable for challenging problems.
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