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Initialize policy parameter θ0 and value function parameter φ0. fori=0,1,2,... do Simulatecurrentpolicyπθi untilNtimestepsareobtained. Compute δVt at all timesteps t ∈ {1,2,...,N}, using V = Vφi. Compute Aˆ t = ∞l=0(γλ)lδVt+l at all timesteps. Compute θi+1 with TRPO update, Equation (35). Compute φi+1 with Equation (34). end for 4.6.2 Experimental Setup 4.6 experiments 56 Figure 7: Top figures: robot models used for 3D locomotion. Bottom figures: a sequence of frames from the learned gaits. Videos are available at https://sites.google.com/site/ gaepapersupp. We evaluated our approach on the classic cart-pole balancing problem, as well as several challenging 3D locomotion tasks: (1) bipedal locomotion; (2) quadrupedal loco- motion; (3) dynamically standing up, for the biped, which starts off laying on its back. The models are shown in Figure 7. Architecture We used the same neural network architecture for all of the 3D robot tasks, which was a feedforward network with three hidden layers, with 100, 50 and 25 tanh units respec-PDF Image | OPTIMIZING EXPECTATIONS: FROM DEEP REINFORCEMENT LEARNING TO STOCHASTIC COMPUTATION GRAPHS
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