Reinforcement Learning
De novo Drug Design using Reinforcement Learning with Multiple GPTAgents
De novo drug design is a pivotal issue in pharmacology and a new area of focus in AI for science research. A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates. Although advanced technologies such as transformer models and reinforcement learning have been applied in drug design, their potential has not been fully realized. Therefore, we propose MolRL-MGPT, a reinforcement learning algorithm with multiple GPT agents for drug molecular generation. To promote molecular diversity, we encourage the agents to collaborate in searching for desirable molecules in diverse directions. Our algorithm has shown promising results on the GuacaMol benchmark and exhibits efficacy in designing inhibitors against SARS-CoV-2 protein targets. The codes are available at: https://github.com/HXYfighter/
On the convergence of policy gradient methods to Nash equilibria in general stochastic games Anonymous Author(s) Affiliation Address email
Multi-agent learning in stochastic N-player games is a notoriously difficult problem1 because, in addition to their changing strategic decisions, the players of the game2 must also contend with the fact that the game itself evolves over time, possibly in a3 very complicated manner. Because of this, the equilibrium convergence properties4 of popular learning algorithms - like policy gradient and its variants - are poorly5 understood, except in specific classes of games (such as potential or two-player,6 zero-sum games). In view of all this, we examine the long-run behavior of policy7 gradient methods with respect to Nash equilibrium policies that are second-order8 stationary (SOS) in a sense similar to the type of KKT sufficiency conditions9 used in optimization. Our analysis shows that SOS policies are locally attracting10 with high probability, and we show that policy gradient trajectories with gradient11 estimates provided by the Reinforcealgorithm achieve an O(1/ n) convergence12 rate to such equilibria if the method's step-size is chosen appropriately.
Supplementary Material for " Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning " 1 1 23 14Hyunsoo Chung Jungtaek 23 Kim Boris
In this material, we first describe the importance of action validity prediction networks. Then, we introduce the details of the benchmarks, provide the model architecture, and present the additional experimental results, which are missing in the main article. We present the results of wall-clock time for computing the ground-truth action validity in Figure s.1. It shows that computing the action validity for a combination of 100 bricks needs more than 20 seconds. Moreover, we summarize the comparisons between possible action validation approaches as shown in Table s.1.0
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction
Tree Search (TS) is crucial to some of the most influential successes in reinforcement learning. Here, we tackle two major challenges with TS that limit its usability: distribution shift and scalability. We first discover and analyze a counter-intuitive phenomenon: action selection through TS and a pre-trained value function often leads to lower performance compared to the original pre-trained agent, even when having access to the exact state and reward in future steps. We show this is due to a distribution shift to areas where value estimates are highly inaccurate and analyze this effect using Extreme Value theory. To overcome this problem, we introduce a novel off-policy correction term that accounts for the mismatch between the pre-trained value and its corresponding TS policy by penalizing under-sampled trajectories.
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
Inverse optimal control can be used to characterize behavior in sequential decisionmaking tasks. Most existing work, however, is limited to fully observable or linear systems, or requires the action signals to be known. Here, we introduce a probabilistic approach to inverse optimal control for partially observable stochastic non-linear systems with unobserved action signals, which unifies previous approaches to inverse optimal control with maximum causal entropy formulations. Using an explicit model of the noise characteristics of the sensory and motor systems of the agent in conjunction with local linearization techniques, we derive an approximate likelihood function for the model parameters, which can be computed within a single forward pass.
