Offline RL Edit Task
Playing Games • General Reinforcement Learning
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COMBO: Conservative Offline Model-Based Policy Optimization
We overcome this limitation by developing a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-action tuples generated via rollouts under the learned model.
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
We evaluate existing offline RL algorithms on NeoRL and argue that the performance of a policy should also be compared with the deterministic version of the behavior policy, instead of the dataset reward.
POPO: Pessimistic Offline Policy Optimization
Offline reinforcement learning (RL), also known as batch RL, aims to optimize policy from a large pre-recorded dataset without interaction with the environment.
Batch Exploration with Examples for Scalable Robotic Reinforcement Learning
• stanford-iris-lab/batch-exploration
• 
Concretely, we propose an exploration technique, Batch Exploration with Examples (BEE), that explores relevant regions of the state-space, guided by a modest number of human provided images of important states.
DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs
ICLR 2021
• maximecb/gym-miniworld
•
We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience.
Human-centric Dialog Training via Offline Reinforcement Learning
EMNLP 2020
• natashamjaques/neural_chat
•
We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL).
Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
ICLR 2021
• FOCAL-ICLR/FOCAL-ICLR
•
In this work, we enforce behavior regularization on learned policy as a general approach to offline RL, combined with a deterministic context encoder for efficient task inference.
Conservative Q-Learning for Offline Reinforcement Learning
NeurIPS 2020
• aviralkumar2907/CQL
•
We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees.
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
ICLR 2021
• matsuolab/BREMEN
• 
We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN) that can effectively optimize a policy offline using 10-20 times fewer data than prior works.
Acme: A Research Framework for Distributed Reinforcement Learning
• deepmind/acme
• 
Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.