Papers Partially Observable Reinforcement Learning
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Partially Observable Reinforcement Learning with Memory Traces
Partially observable environments present a considerable computational challenge in reinforcement learning due to the need to consider long histories. Learning with a finite window of observations quickly becomes intract…
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningValue predictionProvable Partially Observable Reinforcement Learning with Privileged Information
Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL). In practice, certain \emph{privileged information}, e.g., the access to states from simulators, ha…
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
In most real-world reinforcement learning applications, state information is only partially observable, which breaks the Markov decision process assumption and leads to inferior performance for algorithms that conflate o…
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningReal-Time Recurrent Reinforcement Learning
We introduce a biologically plausible RL framework for solving tasks in partially observable Markov decision processes (POMDPs). The proposed algorithm combines three integral parts: (1) A Meta-RL architecture, resemblin…
continuous-controlContinuous ControlPartially Observable Reinforcement Learningreinforcement-learning+1POPGym: Benchmarking Partially Observable Reinforcement Learning
Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory. Despite this, partial observability is still largely ignored by contemporary RL benchmarks and libraries. We i…
BenchmarkingGPUPartially Observable Reinforcement Learningreinforcement-learning+4Leveraging Fully Observable Policies for Learning under Partial Observability
Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state information is often possible. In particu…
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Deep Transformer Q-Networks for Partially Observable Reinforcement Learning
Real-world reinforcement learning tasks often involve some form of partial observability where the observations only give a partial or noisy view of the true state of the world. Such tasks typically require some form of …
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)When Is Partially Observable Reinforcement Learning Not Scary?
Applications of Reinforcement Learning (RL), in which agents learn to make a sequence of decisions despite lacking complete information about the latent states of the controlled system, that is, they act under partial ob…
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Reward Machines: A Study in Partially Observable Reinforcement Learning
Reinforcement learning (RL) is a central problem in artificial intelligence. This problem consists of defining artificial agents that can learn optimal behaviour by interacting with an environment -- where the optimal be…
Partially Observable Reinforcement LearningProblem Decompositionreinforcement-learningReinforcement Learning+1Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning
This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems. Rather than compressing sequential information at every timestep as in conventional recurrent neura…
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning What to Memorize: Using Intrinsic Motivation to Form Useful Memory in Partially Observable Reinforcement Learning
Reinforcement Learning faces an important challenge in partial observable environments that has long-term dependencies. In order to learn in an ambiguous environment, an agent has to keep previous perceptions in a memory…
FormPartially Observable Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Unbiased Asymmetric Reinforcement Learning under Partial Observability
In partially observable reinforcement learning, offline training gives access to latent information which is not available during online training and/or execution, such as the system state. Asymmetric actor-critic method…
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Hard Attention Control By Mutual Information Maximization
Biological agents have adopted the principle of attention to limit the rate of incoming information from the environment. One question that arises is if an artificial agent has access to only a limited view of its surrou…
Hard AttentionPartially Observable Reinforcement LearningThe act of remembering: a study in partially observable reinforcement learning
Reinforcement Learning (RL) agents typically learn memoryless policies---policies that only consider the last observation when selecting actions. Learning memoryless policies is efficient and optimal in fully observable …
Partially Observable Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Adaptive Transformers in RL
Recent developments in Transformers have opened new interesting areas of research in partially observable reinforcement learning tasks. Results from late 2019 showed that Transformers are able to outperform LSTMs on both…
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Reward Machines for Partially Observable Reinforcement Learning
Reward Machines (RMs), originally proposed for specifying problems in Reinforcement Learning (RL), provide a structured, automata-based representation of a reward function that allows an agent to decompose problems into …
Partially Observable Reinforcement LearningProblem Decompositionreinforcement-learningReinforcement Learning+1Stabilizing Transformers for Reinforcement Learning
Owing to their ability to both effectively integrate information over long time horizons and scale to massive amounts of data, self-attention architectures have recently shown breakthrough success in natural language pro…
General Reinforcement LearningLanguage ModelingLanguage ModellingMachine Translation+4Learning Partially Observable Deterministic Action Models
We present exact algorithms for identifying deterministic-actions effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model(the way actions affect the world) of…
Partially Observable Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)