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To facilitate efficient learning, policy gradient approaches to deepreinforcement learning (RL) are typically paired with variance reductionmeasures and strategies for making large but safe policy changes based on abatch of experiences. Natural policy gradient methods, including Trust RegionPolicy Optimization (TRPO), seek to produce monotonic improvement throughbounded changes in policy outputs. Proximal Policy Optimization (PPO) is acommonly used, first-order algorithm that instead uses loss clipping to takemultiple safe optimization steps per batch of data, replacing the bound on thesingle step of TRPO with regularization on multiple steps. In this work, wefind that the performance of PPO, when applied to continuous action spaces, maybe consistently improved through a simple change in objective. Instead of theimportance sampling objective of PPO, we instead recommend a basic policygradient, clipped in an equivalent fashion. While both objectives producebiased gradient estimates with respect to the RL objective, they also bothdisplay significantly reduced variance compared to the unbiased off-policypolicy gradient. Additionally, we show that (1) the clipped-objective policygradient (COPG) objective is on average "pessimistic" compared to both the PPOobjective and (2) this pessimism promotes enhanced exploration. As a result, weempirically observe that COPG produces improved learning compared to PPO insingle-task, constrained, and multi-task learning, without adding significantcomputational cost or complexity. Compared to TRPO, the COPG approach is seento offer comparable or superior performance, while retaining the simplicity ofa first-order method.

Clipped-Objective Policy Gradients for Pessimistic Policy Optimization