899 research outputs found
Generalized Off-Policy Actor-Critic
We propose a new objective, the counterfactual objective, unifying existing
objectives for off-policy policy gradient algorithms in the continuing
reinforcement learning (RL) setting. Compared to the commonly used excursion
objective, which can be misleading about the performance of the target policy
when deployed, our new objective better predicts such performance. We prove the
Generalized Off-Policy Policy Gradient Theorem to compute the policy gradient
of the counterfactual objective and use an emphatic approach to get an unbiased
sample from this policy gradient, yielding the Generalized Off-Policy
Actor-Critic (Geoff-PAC) algorithm. We demonstrate the merits of Geoff-PAC over
existing algorithms in Mujoco robot simulation tasks, the first empirical
success of emphatic algorithms in prevailing deep RL benchmarks.Comment: NeurIPS 201
Deep Residual Reinforcement Learning
We revisit residual algorithms in both model-free and model-based
reinforcement learning settings. We propose the bidirectional target network
technique to stabilize residual algorithms, yielding a residual version of DDPG
that significantly outperforms vanilla DDPG in the DeepMind Control Suite
benchmark. Moreover, we find the residual algorithm an effective approach to
the distribution mismatch problem in model-based planning. Compared with the
existing TD() method, our residual-based method makes weaker assumptions
about the model and yields a greater performance boost.Comment: AAMAS 202
The Divergence of Reinforcement Learning Algorithms with Value-Iteration and Function Approximation
This paper gives specific divergence examples of value-iteration for several
major Reinforcement Learning and Adaptive Dynamic Programming algorithms, when
using a function approximator for the value function. These divergence examples
differ from previous divergence examples in the literature, in that they are
applicable for a greedy policy, i.e. in a "value iteration" scenario. Perhaps
surprisingly, with a greedy policy, it is also possible to get divergence for
the algorithms TD(1) and Sarsa(1). In addition to these divergences, we also
achieve divergence for the Adaptive Dynamic Programming algorithms HDP, DHP and
GDHP.Comment: 8 pages, 4 figures. In Proceedings of the IEEE International Joint
Conference on Neural Networks, June 2012, Brisbane (IEEE IJCNN 2012), pp.
3070--307
Addressing Function Approximation Error in Actor-Critic Methods
In value-based reinforcement learning methods such as deep Q-learning,
function approximation errors are known to lead to overestimated value
estimates and suboptimal policies. We show that this problem persists in an
actor-critic setting and propose novel mechanisms to minimize its effects on
both the actor and the critic. Our algorithm builds on Double Q-learning, by
taking the minimum value between a pair of critics to limit overestimation. We
draw the connection between target networks and overestimation bias, and
suggest delaying policy updates to reduce per-update error and further improve
performance. We evaluate our method on the suite of OpenAI gym tasks,
outperforming the state of the art in every environment tested.Comment: Accepted at ICML 201
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
We present the first provably convergent two-timescale off-policy
actor-critic algorithm (COF-PAC) with function approximation. Key to COF-PAC is
the introduction of a new critic, the emphasis critic, which is trained via
Gradient Emphasis Learning (GEM), a novel combination of the key ideas of
Gradient Temporal Difference Learning and Emphatic Temporal Difference
Learning. With the help of the emphasis critic and the canonical value function
critic, we show convergence for COF-PAC, where the critics are linear and the
actor can be nonlinear.Comment: ICML 202
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