3 research outputs found
Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning
A mechanism called Eligibility Propagation is proposed to speed up the Time
Hopping technique used for faster Reinforcement Learning in simulations.
Eligibility Propagation provides for Time Hopping similar abilities to what
eligibility traces provide for conventional Reinforcement Learning. It
propagates values from one state to all of its temporal predecessors using a
state transitions graph. Experiments on a simulated biped crawling robot
confirm that Eligibility Propagation accelerates the learning process more than
3 times.Comment: 7 page
Time manipulation technique for speeding up reinforcement learning in simulations
A technique for speeding up reinforcement learning algorithms by using time
manipulation is proposed. It is applicable to failure-avoidance control
problems running in a computer simulation. Turning the time of the simulation
backwards on failure events is shown to speed up the learning by 260% and
improve the state space exploration by 12% on the cart-pole balancing task,
compared to the conventional Q-learning and Actor-Critic algorithms.Comment: 12 page