The Meta-Representation Hypothesis

Humans rely on high-level meta-representations to engage in abstract reasoning. In complex cognitive tasks, these meta-representations help individuals abstract general rules from experience. However, constructing such meta-representations from high-dimensional observations remains a longstanding challenge for reinforcement learning agents. For instance, a well-trained agent often fails to generalize to even minor variations of the same task, such as changes in background color, while humans can easily handle. In this paper, we build a bridge between meta-representation and generalization, showing that generalization performance benefits from meta-representation learning. We also hypothesize that deep mutual learning (DML) among agents can help them converge to meta-representations. Empirical results provide support for our theory and hypothesis. Overall, this work provides a new perspective on the generalization of deep reinforcement learning.

Simple Policy Optimization

PPO (Proximal Policy Optimization) algorithm has demonstrated excellent performance in many fields, and it is considered as a simple version of TRPO (Trust Region Policy Optimization) algorithm. However, the ratio clipping operation in PPO may not always effectively enforce the trust region constraints, this can be a potential factor affecting the stability of the algorithm. In this paper, we propose SPO (Simple Policy Optimization) algorithm, which introduces a novel clipping method for KL divergence between the old and current policies. SPO can effectively enforce the trust region constraints in almost all environments, while still maintaining the simplicity of a first-order algorithm. Comparative experiments in Atari 2600 environments show that SPO sometimes provides stronger performance than PPO. Code is available at https://github.com/MyRepositories-hub/Simple-Policy-Optimization.

Dropout Strategy in Reinforcement Learning: Limiting the Surrogate Objective Variance in Policy Optimization Methods

Policy-based reinforcement learning algorithms are widely used in various fields. Among them, mainstream policy optimization algorithms such as TRPO and PPO introduce importance sampling into policy iteration, which allows the reuse of historical data. However, this can also lead to a high variance of the surrogate objective and indirectly affects the stability and convergence of the algorithm. In this paper, we first derived an upper bound of the surrogate objective variance, which can grow quadratically with the increase of the surrogate objective. Next, we proposed the dropout technique to avoid the excessive increase of the surrogate objective variance caused by importance sampling. Then, we introduced a general reinforcement learning framework applicable to mainstream policy optimization methods, and applied the dropout technique to the PPO algorithm to obtain the D-PPO variant. Finally, we conduct comparative experiments between D-PPO and PPO algorithms in the Atari 2600 environment, and the results show that D-PPO achieved significant performance improvements compared to PPO, and effectively limited the excessive increase of the surrogate objective variance during training.