**High-Dimensional Continuous Control Using Generalized Advantage Estimation**. Presented by jialun lyu and zhibozhang The two main challenges are the.

Which we call generalized advantage estimation (gae), involves using a. Abstract the two main challenges in policy gradient methods are the large number of samples typically required, and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data. Note that if we set this to 0, then we are left with the td advantage estimate (high bias, low variance) and if we set it to 1, this is the equivalent of choosing i = n for the extended advantage estimate (low bias, high variance).

### The Two Main Challenges Are The.

Which we call generalized advantage estimation (gae), involves using a. Abstract the two main challenges in policy gradient methods are the large number of samples typically required, and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data. Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks.

### We Address The First Challenge By Using Value Functions To Substantially Reduce The Variance Of Policy Gradient Estimates At The Cost Of Some.

Performance after 20 iterations of policy optimization, as γ and λ are varied. Here, λ is the exponential weight discount. This will contain my notes for research papers that i read.

### Presented By Jialun Lyu And Zhibozhang

The two main challenges are the large number. The best results are obtained at intermediate values of both. Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks.

### John Schulman , Philipp Moritz , Sergey Levine , Michael Jordan , Pieter Abbeel (Submitted On 8 Jun 2015 ( V1 ), Last Revised 20 Oct 2018 (This Version, V6))

Schulman, john & moritz, philipp & levine, sergey & jordan, michael & abbeel, pieter. John schulman , philipp moritz , sergey levine , michael jordan , pieter abbeel (submitted on 8 jun 2015 (this version), latest version 20 oct 2018 ( v6 )) Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks.

### Note That If We Set This To 0, Then We Are Left With The Td Advantage Estimate (High Bias, Low Variance) And If We Set It To 1, This Is The Equivalent Of Choosing I = N For The Extended Advantage Estimate (Low Bias, High Variance).

The fastest policy improvement is obtain by intermediate values of λ in the range [0.92, 0.98]. The generalized advantage estimator for 0 < λ < 1 makes a compromise between bias and variance, controlled by parameter λ.