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Found 6 papers, 0 papers with code
Markov flow policy -- deep MC
Discounted algorithms often encounter evaluation errors due to their reliance on short-term estimations, which can impede their efficacy in addressing simple, short-term tasks and impose undesired temporal discounts (\(\gamma\)).
Conservative DDPG -- Pessimistic RL without Ensemble
DDPG is hindered by the overestimation bias problem, wherein its $Q$-estimates tend to overstate the actual $Q$-values.
MinMaxMin $Q$-learning
MinMaxMin $Q$-learning is a novel optimistic Actor-Critic algorithm that addresses the problem of overestimation bias ($Q$-estimations are overestimating the real $Q$-values) inherent in conservative RL algorithms.
SQT -- std $Q$-target
We implement SQT on top of TD3/TD7 code and test it against the state-of-the-art (SOTA) actor-critic algorithms, DDPG, TD3 and TD7 on seven popular MuJoCo and Bullet tasks.
Optimizing Agent Collaboration through Heuristic Multi-Agent Planning
The SOTA algorithms for addressing QDec-POMDP issues, QDec-FP and QDec-FPS, are unable to effectively tackle problems that involve different types of sensing agents.
Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics
In the first stage, we solve a single-agent problem and get a policy.
