In this paper, we investigate motor primitive learning with the Natural Actor-Critic approach. The Natural Actor-Critic consists out of actor updates which are achieved using natural stochastic policy gradients while the critic obtains the natural policy gradient by linear regression. We show that this architecture can be used to learn the “building blocks of movement generation”, called motor ...

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We present the first class of policy-gradient algorithms that work with both state-value and policy function-approximation, and are guaranteed to converge under off-policy training. Our solution targets problems in reinforcement learning where the action representation adds to thecurse-of-dimensionality; that is, with continuous or large action sets, thus making it infeasible to estimate state-...

The SR-based critic learns an estimate of the value function, using the SR as its feature representation. Unlike standard actor-critic methods, the critic does not use reward-based temporal difference errors to update its value estimate; instead, it relies on the fact that the value function is given by V (s) = ∑ s′ M(s, s ′)R(s′), where M is the successor representation andR is the expected re...

In this work, an adaptive critic-based neuro-fuzzy is presented for an unmanned bicycle. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the back propagation of error algorithm to tune online conclusion parts of the fuzz...

We introduce a class of variational actor-critic algorithms based on formulation over both the value function and policy. The objective consists two parts: one for maximizing other minimizing Bellman residual. Besides vanilla gradient descent with policy updates, we propose variants, clipping method flipping method, in order to speed up convergence. also prove that, when prefactor residual is s...

This paper discusses convergence issues when training adaptive critic designs (ACD) to control dynamic systems expressed as Markov sequences. We critically review two published convergence results of critic-based training and propose to shift emphasis towards more practically valuable convergence proofs. We show a possible way to prove convergence of ACD training.