Trust-Region-Free Policy Optimization for Stochastic Policies

Trust Region Policy Optimization (TRPO) is an iterative method that simultaneously maximizes a surrogate objective and enforces a trust region constraint over consecutive policies in each iteration. The combination of the surrogate objective maximization and the trust region enforcement has been shown to be crucial to guarantee a monotonic policy improvement. However, solving a trust-region-constrained optimization problem can be computationally intensive as it requires many steps of conjugate gradient and a large number of on-policy samples. In this paper, we show that the trust region constraint over policies can be safely substituted by a trust-region-free constraint without compromising the underlying monotonic improvement guarantee. The key idea is to generalize the surrogate objective used in TRPO in a way that a monotonic improvement guarantee still emerges as a result of constraining the maximum advantage-weighted ratio between policies. This new constraint outlines a conservative mechanism for iterative policy optimization and sheds light on practical ways to optimize the generalized surrogate objective. We show that the new constraint can be effectively enforced by being conservative when optimizing the generalized objective function in practice. We call the resulting algorithm Trust-REgion-Free Policy Optimization (TREFree) as it is free of any explicit trust region constraints. Empirical results show that TREFree outperforms TRPO and Proximal Policy Optimization (PPO) in terms of policy performance and sample efficiency.Comment: RLDM 202

Trust Region Policy Optimization for POMDPs

We propose Generalized Trust Region Policy Optimization (GTRPO), a policy gradient Reinforcement Learning (RL) algorithm for both Markov decision processes (MDP) and Partially Observable Markov Decision Processes (POMDP). Policy gradient is a class of model-free RL methods. Previous policy gradient methods are guaranteed to converge only when the underlying model is an MDP and the policy is run for an infinite horizon. We relax these assumptions to episodic settings and to partially observable models with memory-less policies. For the latter class, GTRPO uses a variant of the Q-function with only three consecutive observations for each policy updates, and hence, is computationally efficient. We theoretically show that the policy updates in GTRPO monotonically improve the expected cumulative return and hence, GTRPO has convergence guarantees